A Masked Pruning Approach for Dimensionality Reduction in Communication-Efficient Federated Learning Systems

TL;DR

This paper introduces MPFL, a novel federated learning algorithm that combines pruning with communication-efficient model updates, significantly reducing bandwidth while maintaining model performance.

Contribution

The paper proposes a new masked pruning method integrated into federated learning to achieve low-dimensional models with minimal communication overhead.

Findings

MPFL reduces communication costs by transmitting low-dimensional masks.

Experimental results show MPFL outperforms existing communication-efficient FL methods.

Open-source implementation available for further research.

Abstract

Federated Learning (FL) represents a growing machine learning (ML) paradigm designed for training models across numerous nodes that retain local datasets, all without directly exchanging the underlying private data with the parameter server (PS). Its increasing popularity is attributed to notable advantages in terms of training deep neural network (DNN) models under privacy aspects and efficient utilization of communication resources. Unfortunately, DNNs suffer from high computational and communication costs, as well as memory consumption in intricate tasks. These factors restrict the applicability of FL algorithms in communication-constrained systems with limited hardware resources. In this paper, we develop a novel algorithm that overcomes these limitations by synergistically combining a pruning-based method with the FL process, resulting in low-dimensional representations of the model with minimal communication cost, dubbed Masked Pruning over FL (MPFL). The algorithm operates by initially distributing weights to the nodes through the PS. Subsequently, each node locally trains its model and computes pruning masks. These low-dimensional masks are then transmitted back to the PS, which generates a consensus pruning mask, broadcasted back to the nodes. This iterative process enhances the robustness and stability of the masked pruning model. The generated mask is used to train the FL model, achieving significant bandwidth savings. We present an extensive experimental study demonstrating the superior performance of MPFL compared to existing methods. Additionally, we have developed an open-source software package for the benefit of researchers and developers in related fields.