With the rapid development of the Internet of Things (IoT), AI model training on private data such as human sensing data is highly desired. Federated learning (FL) has emerged as a privacy-preserving distributed training framework for this purpuse. However, the data heterogeneity issue among IoT devices can significantly degrade the model performance and convergence speed in FL. Existing approaches limit in fixed client selection and aggregation on cloud server, making the privacy-preserving extraction of client-specific information during local training challenging. To this end, we propose Client-Centric Adaptation federated learning (FedCCA), an algorithm that optimally utilizes client-specific knowledge to learn a unique model for each client through selective adaptation, aiming to alleviate the influence of data heterogeneity. Specifically, FedCCA employs dynamic client selection and adaptive aggregation based on the additional client-specific encoder. To enhance multi-source knowledge transfer, we adopt an attention-based global aggregation strategy. We conducted extensive experiments on diverse datasets to assess the efficacy of FedCCA. The experimental results demonstrate that our approach exhibits a substantial performance advantage over competing baselines in addressing this specific problem.

翻译：随着物联网（IoT）的快速发展，在人类感知数据等私有数据上进行人工智能模型训练的需求日益增长。联邦学习（FL）作为一种隐私保护的分布式训练框架应运而生。然而，物联网设备间的数据异质性问题会显著降低联邦学习中模型的性能与收敛速度。现有方法局限于云端服务器上的固定客户端选择与聚合机制，使得在本地训练过程中隐私保护地提取客户端特定信息具有挑战性。为此，我们提出客户端中心化自适应联邦学习算法（FedCCA），该算法通过选择性自适应，最优地利用客户端特定知识为每个客户端学习一个独特模型，旨在缓解数据异质性的影响。具体而言，FedCCA基于额外的客户端特定编码器，采用动态客户端选择与自适应聚合策略。为增强多源知识迁移，我们采用了基于注意力的全局聚合策略。我们在多个数据集上进行了广泛的实验以评估FedCCA的有效性。实验结果表明，在解决这一特定问题上，我们的方法相较于现有基线模型展现出显著的性能优势。