Fed-HeLLo allocates different LoRA layers to clients of different resource levels using importance scores and geometric patterns, improving federated fine-tuning accuracy over random allocation baselines.
Exact Penalty Method for Federated Learning
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abstract
Federated learning has burgeoned recently in machine learning, giving rise to a variety of research topics. Popular optimization algorithms are based on the frameworks of the (stochastic) gradient descent methods or the alternating direction method of multipliers. In this paper, we deploy an exact penalty method to deal with federated learning and propose an algorithm, FedEPM, that enables to tackle four critical issues in federated learning: communication efficiency, computational complexity, stragglers' effect, and data privacy. Moreover, it is proven to be convergent and testified to have high numerical performance.
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Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation
Fed-HeLLo allocates different LoRA layers to clients of different resource levels using importance scores and geometric patterns, improving federated fine-tuning accuracy over random allocation baselines.