FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Recent research in large language models (LLMs) has focused on enabling clients to fine-tune their locally deployed homogeneous LLMs collaboratively or on transferring knowledge from server-based LLM to small language models at downstream clients. |
| Approach: | They propose a parameter-efficient federated mutual knowledge transfer framework for large and small language models that allows for token alignment and selective knowledge transfer between client-side LLMs and a server-side SLM. |
| Outcome: | The proposed framework enhances the performance of both LLMs and SLMs with clients' unique domain insights while preserving the server's LLM and client's unique domain insight. |
Similar Papers
FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for fine-tuning Large Language Models (LLMs) suffer from a performance bottleneck . Existing approaches like Offsite-Tuning (OT) secure the LLMs IP . |
| Approach: | They propose a framework that replaces weak adapters with a unified, powerful Proxy Small Language Model (SLM) they propose 'resource-friendly' compression and 'robust optimization' to handle data heterogeneity. |
| Outcome: | Experiments show that FedProxy outperforms OT and centralized fine-tuning methods. |
FedCoT: Federated Chain-of-Thought Distillation for Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have emerged as a transformative force in artificial intelligence, demonstrating exceptional proficiency across various tasks. |
| Approach: | They propose a federated framework for the Chain-of-Thought distillation of knowledge from LLMs to SLMs, while adhering to privacy requirements. |
| Outcome: | The proposed framework ensures secure knowledge transfer from an LLM on a high-powered server to an SLM on resource-constrained client while adhering to privacy requirements. |
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) excel in translation and summarization due to the capabilities of transformer architectures. |
| Approach: | They propose to integrate tensorized adapters into model encoder/decoder blocks to improve model adaptability against data heterogeneity. |
| Outcome: | Experiments on large-scale cross-device FL and large-silo FL show that the proposed methods perform on par or even better than existing federated PEFT approaches while reducing communication cost. |
PPC-GPT: Federated Task-Specific Compression of Large Language Models via Pruning and Chain-of-Thought Distillation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are becoming more popular and are gaining widespread use in artificial intelligence. |
| Approach: | They propose a unified framework that addresses both privacy preservation and model compression in federated settings. |
| Outcome: | The proposed framework maintains competitive performance comparable to full-sized LLMs while ensuring robust privacy protection through its federated architecture. |
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization (2023.emnlp-main)
Copied to clipboard
| Challenge: | Prompt tuning of Large Language Models (LLMs) can incur performance degradation or low training efficiency. |
| Approach: | They propose a prompt tuning approach with Adaptive Optimization to enable efficient FL of LLMs. |
| Outcome: | The proposed approach improves performance and efficiency simultaneously and addresses client drift problems on both the device and server sides. |
FedSpaLLM: Federated Pruning of Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing pruning methods assume public access to calibration data, which is impractical for privacy-sensitive applications. |
| Approach: | They propose a federated learning framework for pruning LLMs that prunes models locally based on private data while accounting for system heterogeneity and communication efficiency. |
| Outcome: | The proposed framework reduces communication overhead and personalizes pruning process based on client resources in federated settings. |
Plug-in and Fine-tuning: Bridging the Gap between Small Language Models and Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) are renowned for their extensive linguistic knowledge and strong generalization capabilities, but their high computational demands make them unsuitable for resource-constrained environments. |
| Approach: | They propose a framework that integrates a single frozen layer from an LLM into a SLM and fine-tunes the combined model for specific tasks. |
| Outcome: | The proposed framework improves performance across a range of natural language processing tasks, including both natural language understanding and generation. |
FuseChat: Knowledge Fusion of Chat Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are costly and require significant computational resources and time. |
| Approach: | They propose a fuse-and-merge framework for the knowledge fusion of chat LLMs . they conduct pairwise knowledge fusing on source chat LRMs to create multiple target LLM . |
| Outcome: | The proposed framework is superior to baselines of various sizes. |
Cool-Fusion: Fuse Large Language Models without Training (2025.acl-long)
Copied to clipboard
| Challenge: | Cool-Fusion is a simple yet effective approach to combine two or more heterogeneous large language models . |
| Approach: | They propose a method that fuses the knowledge of two or more heterogeneous large language models to leverage complementary strengths. |
| Outcome: | The proposed method increases accuracy from three strong source LLMs on GSM8K by 17.4%. |
GKT: A Novel Guidance-Based Knowledge Transfer Framework For Efficient Cloud-edge Collaboration LLM Deployment (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods of acceleration require fine-tuning of considerably large models, such as Llama-7B, posing a challenge for average users. |
| Approach: | They propose a Guidance-based Knowledge Transfer framework that leverages a larger LLM as a 'teacher' and a smaller 'student' model to finalize responses. |
| Outcome: | The proposed framework achieves a maximum accuracy improvement of 14.18%, along with a 10.72 times speed-up on GSM8K and an accuracy improvement 14.00% along with 7.73 times speed up in CSQA. |