RLFL: A Reinforcement Learning Aggregation Approach for Hybrid Federated Learning Systems Using Full and Ternary Precision

Abstract

Federated Learning (FL) has emerged as an approach to provide a privacy-preserving and communication-efficient Machine Learning (ML) framework in mobile-edge environments which are likely to be resource-constrained and heterogeneous. Therefore, the required precision level and performance from each of the devices may vary depending upon the circumstances, giving rise to designs containing mixed-precision and quantized models. Among the various quantization schemes, binary and ternary representations are significant since they enable arrangements that can strike effective balances between performance and precision. In this paper, we propose RLFL, a hybrid ternary/full-precision FL system along with a Reinforcement Learning (RL) aggregation method with the goal of improved performance comparing to a homogeneous ternary environment. This system consists a mix of clients with full-precision and resource-constrained clients with ternary ML models. However, aggregating models with ternary and full-precision weights using traditional aggregation approaches present a challenge due to the disparity in weight magnitudes. In order to obtain an improved accuracy, we use a deep RL model to explore and optimize the amount of contribution assigned to each client’s model for aggregation in each iteration. We evaluate and compare accuracy and communication overhead of the proposed approach against the prior work for the classification of MNIST, FMNIST, and CIFAR10 datasets. Evaluation results show that the proposed RLFL system, along with its aggregation technique, outperforms the existing FL approaches in accuracy ranging from 5% to 19% while imposing negligible computation overhead.