Adaptive Asynchronous Federated Learning with Convolutional Autoencoders in Heterogeneous Systems

Federated Learning (FL) has emerged as a promising solution for distributed machine learning in various systems, enabling collaborative model training while preserving data privacy. However, traditional synchronous FL approaches face challenges in heterogeneous environments where clients have varying computational capabilities and network conditions. This paper proposes a novel asynchronous FL framework with convolutional autoencoders (CAEs) for unsupervised representation learning (URL) in distributed systems. Unlike conventional FL methods that require synchronous updates from all clients, our approach enables flexible client participation based on their update history, making it particularly suitable for real-time scenarios where continuous model improvement is critical. The framework addresses key challenges in diverse systems: privacy preservation, unlabeled data handling, and system heterogeneity. Experimental results in the MNIST dataset demonstrate that our method achieves faster convergence and lower training time compared to centralized learning (CL), individual learning (IL), and traditional FL approaches, such as Federated Averaging (FedAvg) and FedProx, while maintaining comparable accuracy.