With the advent of interconnected and sensor-equipped edge devices, Federated Learning (FL) has gained significant attention, enabling decentralized learning while maintaining data privacy. However, FL faces two challenges in real-world tasks: expensive data labeling and domain shift between source and target samples. In this paper, we introduce a privacy-preserving, resource-efficient FL concept for client adaptation in hardware-constrained environments. Our approach includes server model pre-training on source data and subsequent fine-tuning on target data via low-end clients. The local client adaptation process is streamlined by probabilistic mixing of instance-level feature statistics approximated from source and target domain data. The adapted parameters are transferred back to the central server and globally aggregated. Preliminary results indicate that our method reduces computational and transmission costs while maintaining competitive performance on downstream tasks.

翻译：随着互联且配备传感器的边缘设备的出现，联邦学习（Federated Learning, FL）因能在保护数据隐私的同时实现去中心化学习而受到广泛关注。然而，FL在实际任务中面临两大挑战：昂贵的数据标注成本以及源样本与目标样本之间的领域偏移。本文提出了一种在硬件受限环境下兼顾隐私保护与资源效率的联邦学习概念，用于客户端自适应。我们的方法包括在源数据上对服务器模型进行预训练，随后通过低端客户端在目标数据上对模型进行微调。通过从源域和目标域数据中近似得到的实例级特征统计量的概率混合，本地客户端自适应过程得以简化。自适应后的参数被传输回中央服务器并全局聚合。初步结果表明，该方法在降低计算与传输成本的同时，在下游任务中保持了竞争力的性能。