Abstract
With the rapid advancement of the Internet and big data technologies, traditional centralized machine learning methods are challenged when dealing with large-scale datasets. Federated Learning (FL), as an emerging distributed machine learning paradigm, enables multiple clients to collaboratively train a global model while preserving privacy. Edge computing, also recognized as a critical technology for handling massive datasets, has garnered significant attention. However, the heterogeneity of clients in edge computing environments can severely impact the performance of the resultant models. This study introduces an Adaptive Personalized Client-Selection and Model-Aggregation Algorithm, APCSMA, aimed at optimizing FL performance in edge computing settings. The algorithm evaluates clients' contributions by calculating the real-time performance of local models and the cosine similarity between local and global models, and it designs a ContriFunc function to quantify each client's contribution. The server then selects clients and assigns weights during model aggregation based on these contributions. Moreover, the algorithm accommodates personalized needs in local model updates, rather than simply overwriting with the global model. Extensive experiments were conducted on the FashionMNIST and Cifar-10 datasets, simulating three data distributions with parameters dir = 0.1, 0.3, and 0.5. The accuracy improvements achieved were 3.9%, 1.9%, and 1.1% for the FashionMNIST dataset, and 31.9%, 8.4%, and 5.4% for the Cifar-10 dataset, respectively.