Adaptive client training scale orchestration for federated learning

Federated learning (FL), in contrast to traditional centralized learning, has gained significant attention as it enables the training of high-performance neural networks by maintaining user data locally while exchanging model updates between the server and clients, i.e., end-to-end network exchange. However, in practical environment, when considering various anonymous participants' computational resources, only a minority of clients can comply with the constraints imposed during the server-side training process. To address this issue, we propose Adaptive Scaling-Federated Learning (AS-Fed) based on Deep Q-Network (DQN) to dynamically orchestrate client's local data, allowing the inclusion of a larger number of clients in the training procedure. Our experimental results demonstrate that the proposed AS-Fed approach outperforms the legacy scheme, achieving higher normalization performance during the training process.