FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devices

TL;DR

FedCCA introduces a client-centric federated learning approach that leverages adaptive strategies and attention mechanisms to effectively handle data heterogeneity among IoT devices, improving model performance and convergence.

Contribution

The paper presents FedCCA, a novel federated learning algorithm that uses client-specific adaptation and attention-based aggregation to better manage data heterogeneity in IoT environments.

Findings

FedCCA outperforms existing methods in diverse datasets.

Adaptive client selection improves model personalization.

Attention-based aggregation enhances knowledge transfer.

Abstract

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.