FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout

TL;DR

FedDD tackles the communication bottleneck of synchronous federated learning by replacing client selection with differential parameter dropout. It introduces a convex dropout-rate allocation that accounts for system, data, and model heterogeneity, and an uploaded-parameter selection mechanism that prioritizes informative parameters under per-client budgets. The paper provides a convergence analysis under standard assumptions and demonstrates through extensive experiments that FedDD substantially speeds up time-to-accuracy while preserving or improving generalization, including on rare classes, and showing robustness to dynamic communication budgets. This framework enables more clients to contribute meaningful updates with sparse models, delivering practical improvements for resource-constrained FL deployments.

Abstract

Federated Learning (FL) requires frequent exchange of model parameters, which leads to long communication delay, especially when the network environments of clients vary greatly. Moreover, the parameter server needs to wait for the slowest client (i.e., straggler, which may have the largest model size, lowest computing capability or worst network condition) to upload parameters, which may significantly degrade the communication efficiency. Commonly-used client selection methods such as partial client selection would lead to the waste of computing resources and weaken the generalization of the global model. To tackle this problem, along a different line, in this paper, we advocate the approach of model parameter dropout instead of client selection, and accordingly propose a novel framework of Federated learning scheme with Differential parameter Dropout (FedDD). FedDD consists of two key modules: dropout rate allocation and uploaded parameter selection, which will optimize the model parameter uploading ratios tailored to different clients' heterogeneous conditions and also select the proper set of important model parameters for uploading subject to clients' dropout rate constraints. Specifically, the dropout rate allocation is formulated as a convex optimization problem, taking system heterogeneity, data heterogeneity, and model heterogeneity among clients into consideration. The uploaded parameter selection strategy prioritizes on eliciting important parameters for uploading to speedup convergence. Furthermore, we theoretically analyze the convergence of the proposed FedDD scheme. Extensive performance evaluations demonstrate that the proposed FedDD scheme can achieve outstanding performances in both communication efficiency and model convergence, and also possesses a strong generalization capability to data of rare classes.

Figures (21)

• Figure 1: Model training process of FedDD.
• Figure 2: The performance under different data distributions.
• Figure 3: The relationship between training loss and model size.
• Figure 4: Curves of top-1 accuracy under IID and model-homogeneous setting in simulation.
• Figure 5: Curves of top-1 accuracy under Non-IID-a and model-homogeneous setting in simulation.