Adaptive Period Control for Communication Efficient and Fast Convergent Federated Learning

Federated Learning is particularly challenging in IoT environments, where edge and cloud nodes have imbalanced computation capacity and networking bandwidth. The main scalability barrier in distributed stochastic gradient descent-based machine learning frameworks is the communication overhead from frequent model parameter exchanges between workers and the central server. One way to reduce this overhead is by employing constant and periodic averaging, which sends model parameters to the server after a few iterations of local updates from workers. However, investigations have shown that the optimal communication period for balancing communication and convergence is not constant. Although some studies have explored the effectiveness of federated learning with a constant period, dynamically adjusting the period for optimal convergence remains under-explored. To address this, we investigate the impact of the period on global model convergence and propose an adaptive period control mechanism (AdaPC). This mechanism adaptively adjusts the aggregation period of the federated learning framework to achieve fast convergence with minimal communication. Our theoretical and empirical findings demonstrate that our proposed solution achieves faster convergence, lower final training loss, and minimized communication overhead compared to the constant period averaging strategy and other existing solutions.