Recurrent Early Exits for Federated Learning with Heterogeneous Clients
<|reference_start|>Recurrent Early Exits for Federated Learning with Heterogeneous Clients: Federated learning (FL) has enabled distributed learning of a model across multiple clients in a privacy-preserving manner. One of the main challenges of FL is to accommodate clients with varying hardware capacities; clients have differing compute and memory requirements. To tackle this challenge, recent state-of-the-art approaches leverage the use of early exits. Nonetheless, these approaches fall short of mitigating the challenges of joint learning multiple exit classifiers, often relying on hand-picked heuristic solutions for knowledge distillation among classifiers and/or utilizing additional layers for weaker classifiers. In this work, instead of utilizing multiple classifiers, we propose a recurrent early exit approach named ReeFL that fuses features from different sub-models into a single shared classifier. Specifically, we use a transformer-based early-exit module shared among sub-models to i) better exploit multi-layer feature representations for task-specific prediction and ii) modulate the feature representation of the backbone model for subsequent predictions. We additionally present a per-client self-distillation approach where the best sub-model is automatically selected as the teacher of the other sub-models at each client. Our experiments on standard image and speech classification benchmarks across various emerging federated fine-tuning baselines demonstrate ReeFL's effectiveness over previous works.<|reference_end|>
@article{lee2024recurrent,
    title={Recurrent Early Exits for Federated Learning with Heterogeneous Clients},
    author={Royson Lee, Javier Fernandez-Marques, Shell Xu Hu, Da Li, Stefanos
  Laskaridis, {L}ukasz Dudziak, Timothy Hospedales, Ferenc Husz'ar, Nicholas
  D. Lane},
    journal={arXiv preprint arXiv:2405.14791},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14791},
    primaryClass={cs.LG cs.CV cs.DC}
}