Approximate Computing in Federated Learning Settings

Abstract

Approximate Computing has emerged as a promising solution to address the increas ing computational demands of modern applications by allowing controlled inaccu racies. This thesis explores the integration of Approximate Computing into Feder ated Learning (FL), a decentralized machine learning framework designed to protect data privacy. The proposed method introduces an Approximate Stochastic Gradient Descent (SGD) with Batch Averaging (BatchAvg) aggregation, reducing communi cation and computational costs while maintaining model performance. By utilizing techniques like Fixed Point with Error Compensation (FPEC) and BiScaled-DNN, the 32-bit floating-point weights are quantized to 8-bit representations, minimiz ing energy consumption and bandwidth usage. This approach mimics the effects of stragglers in FL, allowing resource-constrained devices to participate effectively in the learning process. Evaluations using the CIFAR-10 dataset demonstrate that this method achieves significant energy and bandwidth savings with only minimal impact on model accuracy. The results indicate the potential for Approximate Computing to improve the scalability and efficiency of Federated Learning, especially in edge computing environments.