FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clustering

• URL: http://arxiv.org/abs/2404.15384v1
• Date: Tue, 23 Apr 2024 10:50:38 GMT
• Title: FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clustering
• Authors: Siqi Ping, Yuzhu Mao, Yang Liu, Xiao-Ping Zhang, Wenbo Ding,
• Abstract summary: Federated Learning (FL) offers a promising solution by enabling fine-tuning across large-scale clients with a variety of task data. This paper addresses the high communication cost for fine-tuning large pre-trained models within FL frameworks through low-rank fine-tuning.
• Score: 12.417857960556155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although large-scale pre-trained models hold great potential for adapting to downstream tasks through fine-tuning, the performance of such fine-tuned models is often limited by the difficulty of collecting sufficient high-quality, task-specific data. Federated Learning (FL) offers a promising solution by enabling fine-tuning across large-scale clients with a variety of task data, but it is bottlenecked by significant communication overhead due to the pre-trained models' extensive size. This paper addresses the high communication cost for fine-tuning large pre-trained models within FL frameworks through low-rank fine-tuning. Specifically, we train a low-rank adapter for each individual task on the client side, followed by server-side clustering for similar group of adapters to achieve task-specific aggregation. Extensive experiments on various language and vision tasks, such as GLUE and CIFAR-10/100, reveal the evolution of task-specific adapters throughout the FL training process and verify the effectiveness of the proposed low-rank task-specific adapter clustering (TAC) method.

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