Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning With Heterogeneous LoRA Allocation.

Federated learning (FL) has recently been used to collaboratively fine-tune foundation models (FMs) across multiple clients. Notably, federated low-rank adaptation (LoRA)-based fine-tuning methods have recently gained attention, which allows clients to fine-tune FMs with a small portion of trainable parameters locally. However, most existing methods do not account for the heterogeneous resources of clients or lack an effective local training strategy to maximize global fine-tuning performance under limited resources. In this work, we propose federated LoRA-based fine-tuning framework with heterogeneous LoRA allocation (Fed-HeLLo), a novel federated LoRA-based fine-tuning framework that enables clients to collaboratively fine-tune an FM with different local trainable LoRA layers. To ensure its effectiveness, we develop several heterogeneous LoRA allocation (HLA) strategies that adaptively allocate local trainable LoRA layers based on clients' resource capabilities and the layer importance. Specifically, based on the dynamic layer importance, we design a Fisher information matrix score-based HLA (FIM-HLA) that leverages dynamic gradient norm information. To better stabilize the training process, we consider the intrinsic importance of LoRA layers and design a geometrically defined HLA (GD-HLA) strategy. It shapes the collective distribution of trainable LoRA layers into specific geometric patterns, such as triangle, inverted triangle, bottleneck, and uniform. Moreover, we extend GD-HLA into a randomized version, named randomized GD-HLA (RGD-HLA), for enhanced model accuracy with randomness. By codesigning the proposed HLA strategies, we incorporate both the dynamic and intrinsic layer importance into the design of our HLA strategy. To thoroughly evaluate our approach, we simulate various complex federated LoRA-based fine-tuning settings using five datasets and three levels of data distributions ranging from independent identically distributed (i.i.d.) to extreme non-i.i.d. The experimental results demonstrate the effectiveness and efficiency of Fed-HeLLo with the proposed HLA strategies. The code is available at https://github.com/ TNI-playground/Fed_HeLLo.