FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models

Tao Fan, Guoqiang Ma, Yan Kang, Hanlin Gu, Yuanfeng Song, Lixin Fan, Kai Chen, Qiang Yang·June 04, 2024

Summary

FedMKT is a federated learning framework that enhances both large language models (LLMs) on servers and smaller models (SLMs) on clients by adaptively transferring knowledge between them. It uses selective mutual knowledge transfer and token alignment via minimum edit distance to address model heterogeneity and domain-specific knowledge. The framework, which evaluates performance on NLP tasks, improves SLMs' performance and enables LLMs to achieve competitive results with less data. FedMKT is evaluated through extensive experiments, showing its effectiveness across diverse scenarios, including scenarios with model heterogeneity and in preserving privacy. The study highlights the benefits of FedMKT in terms of performance enhancement, computational efficiency, and privacy protection, while also suggesting areas for future research in optimizing trade-offs and addressing privacy concerns.

Paper digest

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

The paper aims to address the challenges faced by Large Language Models (LLMs) in domain-specific applications, such as domain-specific knowledge privacy, constrained computing resources, and mutual knowledge transfer between LLMs and Small Language Models (SLMs) . The paper introduces FedMKT, a novel federated mutual knowledge transfer framework designed to enhance the performance of both large and small language models by facilitating effective mutual knowledge transfer between clients' SLMs and the server's LLM . While the challenges addressed in the paper are not entirely new, the approach of using federated learning and knowledge distillation in a selective mutual knowledge transfer process to enhance both LLMs and SLMs simultaneously is a novel contribution .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that a novel federated mutual knowledge transfer framework, called FedMKT, can enhance the performance of both large language models (LLMs) and small language models (SLMs) by facilitating effective knowledge transfer between them . The framework focuses on selectively transferring knowledge between the server's LLM and clients' SLMs, enriching the LLM with clients' domain insights while improving the SLMs' performance through knowledge distillation and token alignment techniques . Through extensive experiments across different scenarios and NLP text generation tasks, the paper seeks to demonstrate that FedMKT can simultaneously boost the performance of both LLMs and SLMs, addressing the challenges of model heterogeneity and enhancing overall capabilities .


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

The paper "FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models" proposes several innovative ideas, methods, and models to enhance federated learning for large and small language models (LLMs and SLMs) . Here are the key contributions of the paper:

  1. FedMKT Framework: The paper introduces the FedMKT framework, which enables effective knowledge transfer between the server's LLM and clients' SLMs, simultaneously enhancing both types of models . This framework fills the gap by facilitating mutual enhancement of both the server's LLM and the clients' SLMs .

  2. Selective Knowledge Transfer and Token Alignment: FedMKT implements a selective knowledge transfer mechanism that distills knowledge from informative SLMs to the server's LLM and vice versa. Additionally, it incorporates a token alignment technique using minimum edit distance (MinED) to ensure efficient knowledge transfer between LLM and SLMs .

  3. Empirical Evaluation and Performance Enhancement: Extensive experiments conducted in various scenarios demonstrate the competitive performance of FedMKT across a wide range of NLP text-generation tasks. The framework is evaluated with heterogeneous, homogeneous, and one-to-one settings, showing significant performance enhancement for SLMs and comparable results for the LLM .

The paper also discusses related work in the field, such as Model Heterogeneous Federated Learning (MHFL) and Federated Learning for LLMs, highlighting the importance of addressing heterogeneity in model architectures and leveraging parameter-efficient fine-tuning methods for federated learning with LLMs . These approaches aim to optimize communication overhead, fine-tuning costs, and model adaptation in federated learning scenarios involving large language models . The "FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models" paper introduces several key characteristics and advantages compared to previous methods:

  1. Federated Mutual Knowledge Transfer Framework: FedMKT presents a novel federated mutual knowledge transfer framework that enhances both the server's Large Language Model (LLM) and clients' Small Language Models (SLMs) simultaneously. This framework fills the gap by facilitating effective knowledge transfer between the LLM on the server and SLMs on clients, leading to mutual enhancement .

  2. Selective Knowledge Transfer and Token Alignment: FedMKT implements a selective knowledge transfer mechanism that distills knowledge from informative SLMs to the server's LLM and vice versa. Additionally, it incorporates a token alignment technique using minimum edit distance (MinED) to ensure efficient knowledge transfer between LLM and SLMs, addressing model heterogeneity and enhancing performance .

  3. Performance Enhancement: Through extensive empirical evaluations across heterogeneous, homogeneous, and one-to-one settings, FedMKT demonstrates significant performance improvements. For instance, FedMKT outperforms Zero-Shot and Standalone methods, achieving notable performance gains on various NLP tasks such as Bloom-1.1B and LLaMa2-1.3B SLMs. The framework also shows competitive results compared to Centralized scenarios, highlighting its effectiveness in knowledge transfer and model enhancement .

  4. Efficiency and Adaptability: By leveraging parameter-efficient fine-tuning (PEFT) methods, FedMKT enables FL clients to adapt LLMs to their specific needs efficiently while minimizing communication overhead and fine-tuning costs. This approach enhances the adaptability of models to diverse requirements while optimizing resource utilization .

In summary, FedMKT stands out for its innovative framework that facilitates mutual knowledge transfer, selective transfer mechanisms, token alignment techniques, and significant performance enhancements across various NLP tasks, showcasing its efficiency, adaptability, and effectiveness in federated learning scenarios involving large and small language models .


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 mutual knowledge transfer for large and small language models. Noteworthy researchers in this area include Tao Fan, Guoqiang Ma, Yan Kang, Hanlin Gu, Yuanfeng Song, Lixin Fan, Kai Chen, and Qiang Yang . The key solution proposed in the paper is FedMKT, a parameter-efficient federated mutual knowledge transfer framework designed to enhance both large language models (LLMs) and small language models (SLMs) simultaneously. FedMKT facilitates adaptive knowledge transfer from the server's LLM to clients' SLMs while enriching the LLM with clients' unique domain insights. The framework leverages token alignment using minimum edit distance and selective mutual knowledge transfer between client-side SLMs and a server-side LLM to collectively enhance their performance .


How were the experiments in the paper designed?

The experiments in the paper were designed by setting up a federated learning scenario involving four clients and one server to evaluate the FedMKT using various publicly available Large Language Models (LLMs) and Small Language Models (SLMs) . The models evaluated in the experiments included LLaMa2-7B, GPT-2-xlarge, OPT-1.3B, Bloom-1.1B, and LLaMa2-1.3B across three distinct scenarios: Heterogeneous, Homogeneous, and One-to-One . The evaluation was conducted on 6 Question Answering (QA) datasets and 2 instruction-following datasets to comprehensively assess the FedMKT framework .


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

The dataset used for quantitative evaluation in the study is primarily based on Accuracy as the evaluation metric for QA datasets and Rouge-L for instruction-following datasets . The code for the study is open source and available on GitHub at the following link: https://github.com/huggingface/peft .


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 extensive experiments across three distinct scenarios to evaluate the effectiveness of FedMKT using various public LLMs and SLMs on a range of NLP text generation tasks . The empirical results demonstrated that FedMKT simultaneously boosted the performance of both LLMs and SLMs . Additionally, the experiments involved a federated learning scenario with four clients and one server, evaluating FedMKT using various publicly available LLMs and SLMs in different settings, which provided a comprehensive analysis of the framework's performance . The study also compared FedMKT against different baselines, showcasing its effectiveness in enhancing the performance of both large and small language models . Overall, the experiments and results in the paper offer substantial evidence to validate the scientific hypotheses and demonstrate the efficacy of the FedMKT framework in mutual knowledge transfer for large and small language models.


What are the contributions of this paper?

The paper "FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models" makes the following contributions:

  • Federated Mutual Knowledge Transfer Framework: The paper introduces a novel framework called FedMKT that facilitates effective knowledge transfer between a server's Large Language Model (LLM) and clients' Small Language Models (SLMs), enhancing the performance of both .
  • Selective Knowledge Transfer and Token Alignment: FedMKT implements a selective knowledge transfer mechanism that distills knowledge from informative SLMs to the server's LLM and vice versa. It also incorporates a token alignment technique using minimum edit distance (MinED) to address model heterogeneity, ensuring efficient knowledge transfer .
  • Empirical Evaluation and Performance Enhancement: Extensive experiments conducted with various publicly available LLMs and SLMs demonstrate the competitive performance of FedMKT across a wide range of Natural Language Processing (NLP) text-generation tasks. The framework is evaluated in heterogeneous, homogeneous, and one-to-one settings, showing significant performance enhancements for SLMs and comparable results for the LLM .

What work can be continued in depth?

Further research in the field of federated large language models (LLMs) can be expanded in several areas based on the existing literature:

  • Investigating the trade-off between utility and efficiency in federated learning for LLMs, particularly focusing on the challenges related to domain-specific knowledge, privacy, and model heterogeneity between LLMs and small language models (SLMs) .
  • Exploring the simultaneous mutual enhancement of both server-side LLMs and client-side SLMs through frameworks like FedMKT, which facilitate adaptive knowledge transfer and token alignment to collectively enhance the performance of both types of models .
  • Addressing the gaps in mutual knowledge transfer between LLMs and SLMs, especially in the context of domain-specific applications, to enhance the comprehensive capabilities of large and small language models .
  • Delving deeper into the challenges posed by model heterogeneity, token alignment, and knowledge transfer processes in federated learning for LLMs to optimize the performance and generalization abilities of these models .
  • Exploring the effectiveness of parameter-efficient federated learning methods like Parameter-Efficient Fine-Tuning (PEFT) in reducing communication overhead and fine-tuning costs for LLMs, enabling efficient adaptation across different tasks while minimizing storage requirements .
  • Investigating the potential of federated transfer learning frameworks like FedMKT to enhance the performance of large language models through selective mutual knowledge transfer processes between server-side LLMs and client-side SLMs, aiming to improve model generalization and domain-specific knowledge incorporation .

Tables

4

Introduction
Background
Overview of federated learning
Challenges in model heterogeneity and domain-specific knowledge
Objective
To develop FedMKT: a framework for knowledge transfer between LLMs and SLMs
Improve SLM performance and enable LLM competitiveness with less data
Address privacy concerns
Method
Data Collection
Selection of diverse NLP tasks for evaluation
Data distribution across clients and servers
Data Preprocessing
Token alignment using minimum edit distance
Handling model heterogeneity
Selective Mutual Knowledge Transfer
Adaptive knowledge transfer mechanism
Addressing differences in model architectures
Evaluation Metrics
Performance enhancement on various tasks
Computational efficiency comparison
Privacy preservation analysis
Experiments and Results
Experiment Design
Model setup: LLMs, SLMs, and FedMKT implementation
Baselines and comparison methods
Performance Analysis
SLM performance improvement
LLM competitiveness with reduced data
Impact on domain-specific tasks
Scalability and Efficiency
Computational load comparison
Communication overhead analysis
Privacy Evaluation
Privacy preservation techniques in FedMKT
Impact on user data confidentiality
Discussion
Benefits and Limitations
Advantages of FedMKT (enhanced performance, efficiency, privacy)
Areas for improvement and future work
Future Research Directions
Optimizing trade-offs between performance and privacy
Addressing privacy concerns in real-world scenarios
Conclusion
Summary of FedMKT's achievements
Implications for NLP and federated learning research
Call to action for further collaboration and advancements.
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
How does FedMKT address model heterogeneity in LLMs and SLMs?
What are the key techniques used by FedMKT for knowledge transfer?
What is FedMKT primarily designed for?
In what ways does FedMKT contribute to the performance of smaller models (SLMs) and large language models (LLMs)?

FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models

Tao Fan, Guoqiang Ma, Yan Kang, Hanlin Gu, Yuanfeng Song, Lixin Fan, Kai Chen, Qiang Yang·June 04, 2024

Summary

FedMKT is a federated learning framework that enhances both large language models (LLMs) on servers and smaller models (SLMs) on clients by adaptively transferring knowledge between them. It uses selective mutual knowledge transfer and token alignment via minimum edit distance to address model heterogeneity and domain-specific knowledge. The framework, which evaluates performance on NLP tasks, improves SLMs' performance and enables LLMs to achieve competitive results with less data. FedMKT is evaluated through extensive experiments, showing its effectiveness across diverse scenarios, including scenarios with model heterogeneity and in preserving privacy. The study highlights the benefits of FedMKT in terms of performance enhancement, computational efficiency, and privacy protection, while also suggesting areas for future research in optimizing trade-offs and addressing privacy concerns.
Mind map
Addressing privacy concerns in real-world scenarios
Optimizing trade-offs between performance and privacy
Areas for improvement and future work
Advantages of FedMKT (enhanced performance, efficiency, privacy)
Impact on user data confidentiality
Privacy preservation techniques in FedMKT
Communication overhead analysis
Computational load comparison
Impact on domain-specific tasks
LLM competitiveness with reduced data
SLM performance improvement
Baselines and comparison methods
Model setup: LLMs, SLMs, and FedMKT implementation
Privacy preservation analysis
Computational efficiency comparison
Performance enhancement on various tasks
Addressing differences in model architectures
Adaptive knowledge transfer mechanism
Handling model heterogeneity
Token alignment using minimum edit distance
Data distribution across clients and servers
Selection of diverse NLP tasks for evaluation
Address privacy concerns
Improve SLM performance and enable LLM competitiveness with less data
To develop FedMKT: a framework for knowledge transfer between LLMs and SLMs
Challenges in model heterogeneity and domain-specific knowledge
Overview of federated learning
Call to action for further collaboration and advancements.
Implications for NLP and federated learning research
Summary of FedMKT's achievements
Future Research Directions
Benefits and Limitations
Privacy Evaluation
Scalability and Efficiency
Performance Analysis
Experiment Design
Evaluation Metrics
Selective Mutual Knowledge Transfer
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Discussion
Experiments and Results
Method
Introduction
Outline
Introduction
Background
Overview of federated learning
Challenges in model heterogeneity and domain-specific knowledge
Objective
To develop FedMKT: a framework for knowledge transfer between LLMs and SLMs
Improve SLM performance and enable LLM competitiveness with less data
Address privacy concerns
Method
Data Collection
Selection of diverse NLP tasks for evaluation
Data distribution across clients and servers
Data Preprocessing
Token alignment using minimum edit distance
Handling model heterogeneity
Selective Mutual Knowledge Transfer
Adaptive knowledge transfer mechanism
Addressing differences in model architectures
Evaluation Metrics
Performance enhancement on various tasks
Computational efficiency comparison
Privacy preservation analysis
Experiments and Results
Experiment Design
Model setup: LLMs, SLMs, and FedMKT implementation
Baselines and comparison methods
Performance Analysis
SLM performance improvement
LLM competitiveness with reduced data
Impact on domain-specific tasks
Scalability and Efficiency
Computational load comparison
Communication overhead analysis
Privacy Evaluation
Privacy preservation techniques in FedMKT
Impact on user data confidentiality
Discussion
Benefits and Limitations
Advantages of FedMKT (enhanced performance, efficiency, privacy)
Areas for improvement and future work
Future Research Directions
Optimizing trade-offs between performance and privacy
Addressing privacy concerns in real-world scenarios
Conclusion
Summary of FedMKT's achievements
Implications for NLP and federated learning research
Call to action for further collaboration and advancements.

Paper digest

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

The paper aims to address the challenges faced by Large Language Models (LLMs) in domain-specific applications, such as domain-specific knowledge privacy, constrained computing resources, and mutual knowledge transfer between LLMs and Small Language Models (SLMs) . The paper introduces FedMKT, a novel federated mutual knowledge transfer framework designed to enhance the performance of both large and small language models by facilitating effective mutual knowledge transfer between clients' SLMs and the server's LLM . While the challenges addressed in the paper are not entirely new, the approach of using federated learning and knowledge distillation in a selective mutual knowledge transfer process to enhance both LLMs and SLMs simultaneously is a novel contribution .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that a novel federated mutual knowledge transfer framework, called FedMKT, can enhance the performance of both large language models (LLMs) and small language models (SLMs) by facilitating effective knowledge transfer between them . The framework focuses on selectively transferring knowledge between the server's LLM and clients' SLMs, enriching the LLM with clients' domain insights while improving the SLMs' performance through knowledge distillation and token alignment techniques . Through extensive experiments across different scenarios and NLP text generation tasks, the paper seeks to demonstrate that FedMKT can simultaneously boost the performance of both LLMs and SLMs, addressing the challenges of model heterogeneity and enhancing overall capabilities .


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

The paper "FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models" proposes several innovative ideas, methods, and models to enhance federated learning for large and small language models (LLMs and SLMs) . Here are the key contributions of the paper:

  1. FedMKT Framework: The paper introduces the FedMKT framework, which enables effective knowledge transfer between the server's LLM and clients' SLMs, simultaneously enhancing both types of models . This framework fills the gap by facilitating mutual enhancement of both the server's LLM and the clients' SLMs .

  2. Selective Knowledge Transfer and Token Alignment: FedMKT implements a selective knowledge transfer mechanism that distills knowledge from informative SLMs to the server's LLM and vice versa. Additionally, it incorporates a token alignment technique using minimum edit distance (MinED) to ensure efficient knowledge transfer between LLM and SLMs .

  3. Empirical Evaluation and Performance Enhancement: Extensive experiments conducted in various scenarios demonstrate the competitive performance of FedMKT across a wide range of NLP text-generation tasks. The framework is evaluated with heterogeneous, homogeneous, and one-to-one settings, showing significant performance enhancement for SLMs and comparable results for the LLM .

The paper also discusses related work in the field, such as Model Heterogeneous Federated Learning (MHFL) and Federated Learning for LLMs, highlighting the importance of addressing heterogeneity in model architectures and leveraging parameter-efficient fine-tuning methods for federated learning with LLMs . These approaches aim to optimize communication overhead, fine-tuning costs, and model adaptation in federated learning scenarios involving large language models . The "FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models" paper introduces several key characteristics and advantages compared to previous methods:

  1. Federated Mutual Knowledge Transfer Framework: FedMKT presents a novel federated mutual knowledge transfer framework that enhances both the server's Large Language Model (LLM) and clients' Small Language Models (SLMs) simultaneously. This framework fills the gap by facilitating effective knowledge transfer between the LLM on the server and SLMs on clients, leading to mutual enhancement .

  2. Selective Knowledge Transfer and Token Alignment: FedMKT implements a selective knowledge transfer mechanism that distills knowledge from informative SLMs to the server's LLM and vice versa. Additionally, it incorporates a token alignment technique using minimum edit distance (MinED) to ensure efficient knowledge transfer between LLM and SLMs, addressing model heterogeneity and enhancing performance .

  3. Performance Enhancement: Through extensive empirical evaluations across heterogeneous, homogeneous, and one-to-one settings, FedMKT demonstrates significant performance improvements. For instance, FedMKT outperforms Zero-Shot and Standalone methods, achieving notable performance gains on various NLP tasks such as Bloom-1.1B and LLaMa2-1.3B SLMs. The framework also shows competitive results compared to Centralized scenarios, highlighting its effectiveness in knowledge transfer and model enhancement .

  4. Efficiency and Adaptability: By leveraging parameter-efficient fine-tuning (PEFT) methods, FedMKT enables FL clients to adapt LLMs to their specific needs efficiently while minimizing communication overhead and fine-tuning costs. This approach enhances the adaptability of models to diverse requirements while optimizing resource utilization .

In summary, FedMKT stands out for its innovative framework that facilitates mutual knowledge transfer, selective transfer mechanisms, token alignment techniques, and significant performance enhancements across various NLP tasks, showcasing its efficiency, adaptability, and effectiveness in federated learning scenarios involving large and small language models .


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 mutual knowledge transfer for large and small language models. Noteworthy researchers in this area include Tao Fan, Guoqiang Ma, Yan Kang, Hanlin Gu, Yuanfeng Song, Lixin Fan, Kai Chen, and Qiang Yang . The key solution proposed in the paper is FedMKT, a parameter-efficient federated mutual knowledge transfer framework designed to enhance both large language models (LLMs) and small language models (SLMs) simultaneously. FedMKT facilitates adaptive knowledge transfer from the server's LLM to clients' SLMs while enriching the LLM with clients' unique domain insights. The framework leverages token alignment using minimum edit distance and selective mutual knowledge transfer between client-side SLMs and a server-side LLM to collectively enhance their performance .


How were the experiments in the paper designed?

The experiments in the paper were designed by setting up a federated learning scenario involving four clients and one server to evaluate the FedMKT using various publicly available Large Language Models (LLMs) and Small Language Models (SLMs) . The models evaluated in the experiments included LLaMa2-7B, GPT-2-xlarge, OPT-1.3B, Bloom-1.1B, and LLaMa2-1.3B across three distinct scenarios: Heterogeneous, Homogeneous, and One-to-One . The evaluation was conducted on 6 Question Answering (QA) datasets and 2 instruction-following datasets to comprehensively assess the FedMKT framework .


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

The dataset used for quantitative evaluation in the study is primarily based on Accuracy as the evaluation metric for QA datasets and Rouge-L for instruction-following datasets . The code for the study is open source and available on GitHub at the following link: https://github.com/huggingface/peft .


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 extensive experiments across three distinct scenarios to evaluate the effectiveness of FedMKT using various public LLMs and SLMs on a range of NLP text generation tasks . The empirical results demonstrated that FedMKT simultaneously boosted the performance of both LLMs and SLMs . Additionally, the experiments involved a federated learning scenario with four clients and one server, evaluating FedMKT using various publicly available LLMs and SLMs in different settings, which provided a comprehensive analysis of the framework's performance . The study also compared FedMKT against different baselines, showcasing its effectiveness in enhancing the performance of both large and small language models . Overall, the experiments and results in the paper offer substantial evidence to validate the scientific hypotheses and demonstrate the efficacy of the FedMKT framework in mutual knowledge transfer for large and small language models.


What are the contributions of this paper?

The paper "FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models" makes the following contributions:

  • Federated Mutual Knowledge Transfer Framework: The paper introduces a novel framework called FedMKT that facilitates effective knowledge transfer between a server's Large Language Model (LLM) and clients' Small Language Models (SLMs), enhancing the performance of both .
  • Selective Knowledge Transfer and Token Alignment: FedMKT implements a selective knowledge transfer mechanism that distills knowledge from informative SLMs to the server's LLM and vice versa. It also incorporates a token alignment technique using minimum edit distance (MinED) to address model heterogeneity, ensuring efficient knowledge transfer .
  • Empirical Evaluation and Performance Enhancement: Extensive experiments conducted with various publicly available LLMs and SLMs demonstrate the competitive performance of FedMKT across a wide range of Natural Language Processing (NLP) text-generation tasks. The framework is evaluated in heterogeneous, homogeneous, and one-to-one settings, showing significant performance enhancements for SLMs and comparable results for the LLM .

What work can be continued in depth?

Further research in the field of federated large language models (LLMs) can be expanded in several areas based on the existing literature:

  • Investigating the trade-off between utility and efficiency in federated learning for LLMs, particularly focusing on the challenges related to domain-specific knowledge, privacy, and model heterogeneity between LLMs and small language models (SLMs) .
  • Exploring the simultaneous mutual enhancement of both server-side LLMs and client-side SLMs through frameworks like FedMKT, which facilitate adaptive knowledge transfer and token alignment to collectively enhance the performance of both types of models .
  • Addressing the gaps in mutual knowledge transfer between LLMs and SLMs, especially in the context of domain-specific applications, to enhance the comprehensive capabilities of large and small language models .
  • Delving deeper into the challenges posed by model heterogeneity, token alignment, and knowledge transfer processes in federated learning for LLMs to optimize the performance and generalization abilities of these models .
  • Exploring the effectiveness of parameter-efficient federated learning methods like Parameter-Efficient Fine-Tuning (PEFT) in reducing communication overhead and fine-tuning costs for LLMs, enabling efficient adaptation across different tasks while minimizing storage requirements .
  • Investigating the potential of federated transfer learning frameworks like FedMKT to enhance the performance of large language models through selective mutual knowledge transfer processes between server-side LLMs and client-side SLMs, aiming to improve model generalization and domain-specific knowledge incorporation .
Tables
4
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