Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training

Zhiyuan Wang, Bokui Chen, Xiaoyang Qu, Zhenhou Hong, Jing Xiao, Jianzong Wang·May 22, 2024

Summary

The paper introduces Federated Split Decision Transformer (FSDT), a privacy-preserving framework for AI agents in decision tasks. FSDT employs a two-stage training process: local embedding and prediction models on client agents, and a global transformer decoder on the server. It addresses the challenges of aggregating data from agents with diverse state variables and action spaces by leveraging distributed data without revealing agent-specific details. The server-side transformer enables efficient data analysis and reduces computational and communication costs. FSDT outperforms centralized and traditional federated learning methods in continuous control tasks using the D4RL dataset, achieving high scores and demonstrating robustness across environments. The study highlights the potential of FSDT for collaborative, privacy-enhanced learning in various intelligent decision-making applications, with future work focusing on expanding its scope and real-world implementation.

Key findings

6

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper addresses the challenge of training multiple intelligent agents from different categories under a federated learning framework by introducing the Federated Split Decision Transformer (FSDT) framework . This framework aims to process and learn from decentralized and heterogeneous offline data generated by these agents, considering the variability in state and action spaces across different agent types . The problem of training personalized intelligent agents with varying state variables and action spaces is not new, but the paper proposes a novel approach, FSDT, to handle this complexity efficiently .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the development and evaluation of a novel split offline reinforcement learning approach called the Federated Split Decision Transformer (FSDT) . The FSDT framework is specifically designed to address the complexities associated with personalized intelligent agents by leveraging distributed data for training while ensuring data privacy . The study focuses on demonstrating the effectiveness of the FSDT approach in achieving high performance while minimizing computational overhead, especially for clients with limited hardware resources .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper introduces a novel approach called Federated Split Decision Transformer (FSDT) designed for training multiple intelligent agents from different categories under a federated learning framework . The FSDT framework is specifically tailored to handle the complexities of personalized intelligent agents by processing and learning from decentralized and heterogeneous offline data generated by these agents . It consists of a two-stage training process where each agent independently trains a local model with an embedding module and a prediction module, and a centralized server with a Transformer decoder synthesizes the received embeddings from different agent types to predict actions .

One key aspect of the proposed method is the utilization of split learning and federated learning techniques to enable efficient training on decentralized sequential data from reinforcement learning algorithms . This approach allows for parallel processing across distributed clients while maintaining model privacy through network splitting and patch shuffling techniques . The FSDT framework addresses the challenge of training multiple intelligent agents with different state and action spaces by incorporating contextual learning within a Markov Decision Process for each agent type .

The paper emphasizes the importance of computational efficiency in enabling clients with limited hardware resources to engage in federated learning, making it an ideal solution for agents operating under resource constraints . The proposed FSDT model aims to enhance learning stability and exploration by predicting actions as Gaussian-distributed vectors, thereby improving the performance of multi-type agent scenarios . Additionally, the paper highlights the potential of the FSDT framework in real-world applications such as autonomous driving and robotics, indicating future research directions to extend the model to handle more complex agent architectures . The Federated Split Decision Transformer (FSDT) framework proposed in the paper offers several key characteristics and advantages compared to previous methods :

  1. Decentralized and Heterogeneous Data Handling: FSDT is designed to process and learn from decentralized and heterogeneous offline data generated by multiple intelligent agents from different categories . This approach addresses the challenge of training agents with varying state and action spaces, ensuring efficient learning from diverse data sources.

  2. Two-Stage Training Process: The FSDT framework involves a two-stage training process where each agent independently trains a local model with an embedding module and a prediction module, while a centralized server with a Transformer decoder synthesizes the received embeddings from different agent types to predict actions . This approach enhances learning stability and exploration by predicting actions as Gaussian-distributed vectors.

  3. Contextual Learning within a Markov Decision Process: Each agent type's learning mechanism in FSDT is conceptualized as contextual learning within a Markov Decision Process, capturing the unique state and action spaces for each agent type . This tailored approach enables effective training of agents with distinct characteristics.

  4. Efficient Federated Learning: FSDT emphasizes computational efficiency, enabling clients with limited hardware resources to engage in federated learning . This efficiency is crucial for agents operating under resource constraints, making FSDT an ideal solution for scenarios where computational resources are limited.

  5. Privacy Preservation: By employing split federated learning algorithms, FSDT leverages distributed data for training without central aggregation, enhancing privacy and security . This approach minimizes the exposure of sensitive trajectory data distributed across multiple client nodes during training.

  6. Performance and Scalability: The FSDT framework demonstrates superior performance in federated split learning for personalized agents, with significant reductions in communication and computational overhead compared to traditional centralized training approaches . This highlights the potential of FSDT in enabling efficient and privacy-preserving collaborative learning in applications like autonomous driving decision systems.

Overall, the FSDT framework stands out for its ability to handle decentralized and heterogeneous data, ensure privacy, enhance computational efficiency, and deliver superior performance in training personalized intelligent agents compared to previous methods.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of federated split learning and decision transformers. Noteworthy researchers in this area include Zhiyuan Wang, Bokui Chen, Xiaoyang Qu, Zhenhou Hong, Jing Xiao, and Jianzong Wang . These researchers have contributed to the development of the Federated Split Decision Transformer (FSDT) framework, which is specifically designed for AI agent decision tasks.

The key to the solution mentioned in the paper is the Federated Split Decision Transformer (FSDT) framework. This framework addresses the challenges of training multiple intelligent agents from different categories under a federated learning framework. The FSDT framework processes and learns from decentralized and heterogeneous offline data generated by these agents. It employs a two-stage training process with local embedding and prediction models on client agents and a global transformer decoder model on the server. This approach enables efficient training on decentralized sequential data using various architectures such as RNNs and Transformers .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the proposed Federated Split Decision Transformer (FSDT) algorithm using the D4RL dataset and the Mujoco simulator with three robot control environments: HalfCheetah, Hopper, and Walker2D . The experiment involved 30 agents divided into 10 agents for each environment . The D4RL dataset, which includes expert, medium, and medium replay levels, was partitioned among the agents following federated learning principles to ensure independent and identically distributed data allocation . Each agent underwent 200 rounds of communication between clients and the server, with 300 steps of local training on the client side and 1000 steps of training on the server side in each round to consolidate learning across all agents . The evaluation metric used was the D4RL score, comparing the performance of the FSDT algorithm against multiple established techniques across different datasets . The study demonstrated that the FSDT algorithm under federated split learning settings outperformed most other methods and achieved performance comparable to traditional centralized training approaches .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the D4RL dataset, which includes expert, medium, and medium replay levels . The code for the proposed algorithm FSDT is not explicitly mentioned to be open source in the provided context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study conducted a comprehensive evaluation using the D4RL dataset to assess the performance of the Federated Split Decision Transformer (FSDT) algorithm in federated split learning for personalized agents . The results demonstrated the superior performance of the FSDT algorithm in training multiple intelligent agents from different categories under a federated learning framework . The evaluation involved 30 agents across different continuous control tasks, ensuring data allocation was independent and identically distributed (IID) among the agents .

The analysis included 200 rounds of communication between clients and the server, with each agent type undergoing local training on the client side followed by training on the server side to consolidate learning across all agents in the federated network . The study employed the D4RL score as an evaluation metric, comparing the performance of the FSDT algorithm against established techniques like Decision Transformer (DT), Conservative Q-Learning (CQL), and others . The results indicated that the FSDT algorithm under federated split learning settings outperformed most other methods and achieved performance comparable to DT in non-federated scenarios .

Furthermore, the experiment analysis included a parameter analysis comparing the FSDT model with the Decision Transformer strategy, highlighting the reduced parameter count of the FSDT due to its context-truncated transformer decoder model . The performance trends of the FSDT algorithm as communication rounds increased were also analyzed, showing that the model converged effectively after around 100 rounds of training . These results collectively provide strong empirical evidence supporting the effectiveness and efficiency of the FSDT algorithm in enabling collaborative learning for intelligent decision-making systems .


What are the contributions of this paper?

The paper "Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training" introduces several key contributions:

  • Federated Split Decision Transformer (FSDT) Framework: The paper presents the FSDT framework designed specifically for AI agent decision tasks, addressing the challenges posed by the variability in state variables and action spaces among personalized agents .
  • Efficient Training with Distributed Data: The FSDT framework utilizes a two-stage training process involving local embedding and prediction models on client agents and a global transformer decoder model on the server, enabling efficient training while preserving data privacy .
  • Superior Performance: The comprehensive evaluation using the benchmark D4RL dataset highlights the superior performance of the FSDT algorithm in federated split learning for personalized agents, with significant reductions in communication and computational overhead compared to traditional centralized training approaches .
  • Privacy Improvements: The implementation of a server-side Transformer decoder in a split learning context enhances performance, potentially leading to privacy improvements as less private data needs to be exposed during training to achieve good performance .
  • Computational Efficiency: The FSDT approach delivers high performance while minimizing overhead, making it suitable for agents operating under resource constraints. This computational efficiency enables clients with limited hardware resources to engage in federated learning .
  • Future Research Directions: The paper suggests future research directions that include extending FSDT to handle more complex agent architectures and exploring applications in real-world scenarios such as autonomous driving and robotics .

What work can be continued in depth?

Future research directions that can be pursued in depth based on the study include:

  • Extending the Federated Split Decision Transformer (FSDT) framework to handle more complex agent architectures, especially in scenarios like autonomous driving and robotics .
  • Exploring applications of the FSDT framework in real-world scenarios to further evaluate its effectiveness and performance in practical settings .
  • Investigating the scalability and adaptability of the FSDT framework for different types of intelligent agents and varying levels of complexity in continuous control tasks .
  • Conducting more comprehensive evaluations and experiments with a larger number of agents and diverse datasets to validate the performance and efficiency of the FSDT algorithm under different conditions .
  • Analyzing the impact of communication rounds on the FSDT model's performance to optimize training processes and enhance learning stability over time .

Tables

2

Introduction
Background
Evolution of AI in decision tasks
Privacy concerns in data sharing
Objective
Introduce FSDT as a solution for privacy and collaboration
Address challenges in federated learning for diverse agents
Method
Local Model Architecture
Client-side Components
Local Embedding Models
Per-agent state and action encoding
Prediction Models
Learning agent-specific decision-making
Diversity Handling
Adaptation to varying state-action spaces
Global Model Architecture
Server-Side Transformer
Transformer Decoder
Aggregation and data analysis without revealing agent details
Communication Efficiency
Reducing computational and bandwidth requirements
Training Process
Two-Stage Training
Local model training on client agents
Global transformer fine-tuning on aggregated embeddings
Distributed Learning
Federated learning without raw data exchange
Performance Evaluation
D4RL Dataset
Continuous control tasks
Comparison with centralized and traditional FL methods
Evaluation metrics: scores and robustness
Results and Discussion
FSDT's performance in decision tasks
Advantages over competing approaches
Real-world applicability
Future Work
Expanding FSDT's scope
Real-world implementation challenges and solutions
Potential extensions and improvements
Conclusion
Summary of FSDT's contributions
Privacy-preserving potential for intelligent decision-making
Implications for collaborative AI research
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
What is the significance of FSDT's performance on the D4RL dataset for collaborative decision-making applications?
What is the primary focus of Federated Split Decision Transformer (FSDT)?
In which type of learning tasks does FSDT demonstrate improved performance compared to centralized and traditional federated learning methods?
How does FSDT ensure privacy while enabling AI agents in decision tasks?

Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training

Zhiyuan Wang, Bokui Chen, Xiaoyang Qu, Zhenhou Hong, Jing Xiao, Jianzong Wang·May 22, 2024

Summary

The paper introduces Federated Split Decision Transformer (FSDT), a privacy-preserving framework for AI agents in decision tasks. FSDT employs a two-stage training process: local embedding and prediction models on client agents, and a global transformer decoder on the server. It addresses the challenges of aggregating data from agents with diverse state variables and action spaces by leveraging distributed data without revealing agent-specific details. The server-side transformer enables efficient data analysis and reduces computational and communication costs. FSDT outperforms centralized and traditional federated learning methods in continuous control tasks using the D4RL dataset, achieving high scores and demonstrating robustness across environments. The study highlights the potential of FSDT for collaborative, privacy-enhanced learning in various intelligent decision-making applications, with future work focusing on expanding its scope and real-world implementation.
Mind map
Adaptation to varying state-action spaces
Diversity Handling
Learning agent-specific decision-making
Evaluation metrics: scores and robustness
Comparison with centralized and traditional FL methods
Continuous control tasks
Global transformer fine-tuning on aggregated embeddings
Local model training on client agents
Reducing computational and bandwidth requirements
Communication Efficiency
Aggregation and data analysis without revealing agent details
Transformer Decoder
Prediction Models
Per-agent state and action encoding
Local Embedding Models
D4RL Dataset
Federated learning without raw data exchange
Distributed Learning
Two-Stage Training
Server-Side Transformer
Client-side Components
Address challenges in federated learning for diverse agents
Introduce FSDT as a solution for privacy and collaboration
Privacy concerns in data sharing
Evolution of AI in decision tasks
Implications for collaborative AI research
Privacy-preserving potential for intelligent decision-making
Summary of FSDT's contributions
Potential extensions and improvements
Real-world implementation challenges and solutions
Expanding FSDT's scope
Real-world applicability
Advantages over competing approaches
FSDT's performance in decision tasks
Performance Evaluation
Training Process
Global Model Architecture
Local Model Architecture
Objective
Background
Conclusion
Future Work
Results and Discussion
Method
Introduction
Outline
Introduction
Background
Evolution of AI in decision tasks
Privacy concerns in data sharing
Objective
Introduce FSDT as a solution for privacy and collaboration
Address challenges in federated learning for diverse agents
Method
Local Model Architecture
Client-side Components
Local Embedding Models
Per-agent state and action encoding
Prediction Models
Learning agent-specific decision-making
Diversity Handling
Adaptation to varying state-action spaces
Global Model Architecture
Server-Side Transformer
Transformer Decoder
Aggregation and data analysis without revealing agent details
Communication Efficiency
Reducing computational and bandwidth requirements
Training Process
Two-Stage Training
Local model training on client agents
Global transformer fine-tuning on aggregated embeddings
Distributed Learning
Federated learning without raw data exchange
Performance Evaluation
D4RL Dataset
Continuous control tasks
Comparison with centralized and traditional FL methods
Evaluation metrics: scores and robustness
Results and Discussion
FSDT's performance in decision tasks
Advantages over competing approaches
Real-world applicability
Future Work
Expanding FSDT's scope
Real-world implementation challenges and solutions
Potential extensions and improvements
Conclusion
Summary of FSDT's contributions
Privacy-preserving potential for intelligent decision-making
Implications for collaborative AI research
Key findings
6

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper addresses the challenge of training multiple intelligent agents from different categories under a federated learning framework by introducing the Federated Split Decision Transformer (FSDT) framework . This framework aims to process and learn from decentralized and heterogeneous offline data generated by these agents, considering the variability in state and action spaces across different agent types . The problem of training personalized intelligent agents with varying state variables and action spaces is not new, but the paper proposes a novel approach, FSDT, to handle this complexity efficiently .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the development and evaluation of a novel split offline reinforcement learning approach called the Federated Split Decision Transformer (FSDT) . The FSDT framework is specifically designed to address the complexities associated with personalized intelligent agents by leveraging distributed data for training while ensuring data privacy . The study focuses on demonstrating the effectiveness of the FSDT approach in achieving high performance while minimizing computational overhead, especially for clients with limited hardware resources .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper introduces a novel approach called Federated Split Decision Transformer (FSDT) designed for training multiple intelligent agents from different categories under a federated learning framework . The FSDT framework is specifically tailored to handle the complexities of personalized intelligent agents by processing and learning from decentralized and heterogeneous offline data generated by these agents . It consists of a two-stage training process where each agent independently trains a local model with an embedding module and a prediction module, and a centralized server with a Transformer decoder synthesizes the received embeddings from different agent types to predict actions .

One key aspect of the proposed method is the utilization of split learning and federated learning techniques to enable efficient training on decentralized sequential data from reinforcement learning algorithms . This approach allows for parallel processing across distributed clients while maintaining model privacy through network splitting and patch shuffling techniques . The FSDT framework addresses the challenge of training multiple intelligent agents with different state and action spaces by incorporating contextual learning within a Markov Decision Process for each agent type .

The paper emphasizes the importance of computational efficiency in enabling clients with limited hardware resources to engage in federated learning, making it an ideal solution for agents operating under resource constraints . The proposed FSDT model aims to enhance learning stability and exploration by predicting actions as Gaussian-distributed vectors, thereby improving the performance of multi-type agent scenarios . Additionally, the paper highlights the potential of the FSDT framework in real-world applications such as autonomous driving and robotics, indicating future research directions to extend the model to handle more complex agent architectures . The Federated Split Decision Transformer (FSDT) framework proposed in the paper offers several key characteristics and advantages compared to previous methods :

  1. Decentralized and Heterogeneous Data Handling: FSDT is designed to process and learn from decentralized and heterogeneous offline data generated by multiple intelligent agents from different categories . This approach addresses the challenge of training agents with varying state and action spaces, ensuring efficient learning from diverse data sources.

  2. Two-Stage Training Process: The FSDT framework involves a two-stage training process where each agent independently trains a local model with an embedding module and a prediction module, while a centralized server with a Transformer decoder synthesizes the received embeddings from different agent types to predict actions . This approach enhances learning stability and exploration by predicting actions as Gaussian-distributed vectors.

  3. Contextual Learning within a Markov Decision Process: Each agent type's learning mechanism in FSDT is conceptualized as contextual learning within a Markov Decision Process, capturing the unique state and action spaces for each agent type . This tailored approach enables effective training of agents with distinct characteristics.

  4. Efficient Federated Learning: FSDT emphasizes computational efficiency, enabling clients with limited hardware resources to engage in federated learning . This efficiency is crucial for agents operating under resource constraints, making FSDT an ideal solution for scenarios where computational resources are limited.

  5. Privacy Preservation: By employing split federated learning algorithms, FSDT leverages distributed data for training without central aggregation, enhancing privacy and security . This approach minimizes the exposure of sensitive trajectory data distributed across multiple client nodes during training.

  6. Performance and Scalability: The FSDT framework demonstrates superior performance in federated split learning for personalized agents, with significant reductions in communication and computational overhead compared to traditional centralized training approaches . This highlights the potential of FSDT in enabling efficient and privacy-preserving collaborative learning in applications like autonomous driving decision systems.

Overall, the FSDT framework stands out for its ability to handle decentralized and heterogeneous data, ensure privacy, enhance computational efficiency, and deliver superior performance in training personalized intelligent agents compared to previous methods.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of federated split learning and decision transformers. Noteworthy researchers in this area include Zhiyuan Wang, Bokui Chen, Xiaoyang Qu, Zhenhou Hong, Jing Xiao, and Jianzong Wang . These researchers have contributed to the development of the Federated Split Decision Transformer (FSDT) framework, which is specifically designed for AI agent decision tasks.

The key to the solution mentioned in the paper is the Federated Split Decision Transformer (FSDT) framework. This framework addresses the challenges of training multiple intelligent agents from different categories under a federated learning framework. The FSDT framework processes and learns from decentralized and heterogeneous offline data generated by these agents. It employs a two-stage training process with local embedding and prediction models on client agents and a global transformer decoder model on the server. This approach enables efficient training on decentralized sequential data using various architectures such as RNNs and Transformers .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the proposed Federated Split Decision Transformer (FSDT) algorithm using the D4RL dataset and the Mujoco simulator with three robot control environments: HalfCheetah, Hopper, and Walker2D . The experiment involved 30 agents divided into 10 agents for each environment . The D4RL dataset, which includes expert, medium, and medium replay levels, was partitioned among the agents following federated learning principles to ensure independent and identically distributed data allocation . Each agent underwent 200 rounds of communication between clients and the server, with 300 steps of local training on the client side and 1000 steps of training on the server side in each round to consolidate learning across all agents . The evaluation metric used was the D4RL score, comparing the performance of the FSDT algorithm against multiple established techniques across different datasets . The study demonstrated that the FSDT algorithm under federated split learning settings outperformed most other methods and achieved performance comparable to traditional centralized training approaches .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the D4RL dataset, which includes expert, medium, and medium replay levels . The code for the proposed algorithm FSDT is not explicitly mentioned to be open source in the provided context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study conducted a comprehensive evaluation using the D4RL dataset to assess the performance of the Federated Split Decision Transformer (FSDT) algorithm in federated split learning for personalized agents . The results demonstrated the superior performance of the FSDT algorithm in training multiple intelligent agents from different categories under a federated learning framework . The evaluation involved 30 agents across different continuous control tasks, ensuring data allocation was independent and identically distributed (IID) among the agents .

The analysis included 200 rounds of communication between clients and the server, with each agent type undergoing local training on the client side followed by training on the server side to consolidate learning across all agents in the federated network . The study employed the D4RL score as an evaluation metric, comparing the performance of the FSDT algorithm against established techniques like Decision Transformer (DT), Conservative Q-Learning (CQL), and others . The results indicated that the FSDT algorithm under federated split learning settings outperformed most other methods and achieved performance comparable to DT in non-federated scenarios .

Furthermore, the experiment analysis included a parameter analysis comparing the FSDT model with the Decision Transformer strategy, highlighting the reduced parameter count of the FSDT due to its context-truncated transformer decoder model . The performance trends of the FSDT algorithm as communication rounds increased were also analyzed, showing that the model converged effectively after around 100 rounds of training . These results collectively provide strong empirical evidence supporting the effectiveness and efficiency of the FSDT algorithm in enabling collaborative learning for intelligent decision-making systems .


What are the contributions of this paper?

The paper "Task-agnostic Decision Transformer for Multi-type Agent Control with Federated Split Training" introduces several key contributions:

  • Federated Split Decision Transformer (FSDT) Framework: The paper presents the FSDT framework designed specifically for AI agent decision tasks, addressing the challenges posed by the variability in state variables and action spaces among personalized agents .
  • Efficient Training with Distributed Data: The FSDT framework utilizes a two-stage training process involving local embedding and prediction models on client agents and a global transformer decoder model on the server, enabling efficient training while preserving data privacy .
  • Superior Performance: The comprehensive evaluation using the benchmark D4RL dataset highlights the superior performance of the FSDT algorithm in federated split learning for personalized agents, with significant reductions in communication and computational overhead compared to traditional centralized training approaches .
  • Privacy Improvements: The implementation of a server-side Transformer decoder in a split learning context enhances performance, potentially leading to privacy improvements as less private data needs to be exposed during training to achieve good performance .
  • Computational Efficiency: The FSDT approach delivers high performance while minimizing overhead, making it suitable for agents operating under resource constraints. This computational efficiency enables clients with limited hardware resources to engage in federated learning .
  • Future Research Directions: The paper suggests future research directions that include extending FSDT to handle more complex agent architectures and exploring applications in real-world scenarios such as autonomous driving and robotics .

What work can be continued in depth?

Future research directions that can be pursued in depth based on the study include:

  • Extending the Federated Split Decision Transformer (FSDT) framework to handle more complex agent architectures, especially in scenarios like autonomous driving and robotics .
  • Exploring applications of the FSDT framework in real-world scenarios to further evaluate its effectiveness and performance in practical settings .
  • Investigating the scalability and adaptability of the FSDT framework for different types of intelligent agents and varying levels of complexity in continuous control tasks .
  • Conducting more comprehensive evaluations and experiments with a larger number of agents and diverse datasets to validate the performance and efficiency of the FSDT algorithm under different conditions .
  • Analyzing the impact of communication rounds on the FSDT model's performance to optimize training processes and enhance learning stability over time .
Tables
2
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