Caesar: Optimizing Federated Learning via Low-deviation Compression
Open Access
- 20 April 2026
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 1763-1774
Abstract
Compression is an efficient way to relieve the tremendous communication overhead of federated learning (FL) systems. However, for the existing works, the information loss under compression will lead to unexpected model/gradient deviation for the FL training, significantly degrading the training performance, especially under the challenges of data heterogeneity and model obsolescence. To strike a delicate trade-off between model accuracy and traffic cost, we propose Caesar, a novel FL framework with a low-deviation compression approach. For the global model download, we design a greedy method to optimize the compression ratio for each device based on the staleness of the local model, ensuring a precise initial model for local training. Regarding the local gradient upload, we utilize the device's local data properties (i.e., sample volume and label distribution) to quantify its local gradient's importance, which then guides the determination of the gradient compression ratio. We have implemented Caesar, on two physical platforms with 40 smartphones and 80 NVIDIA Jetson devices. Extensive results show that Caesar, can reduce the traffic costs by about 25.54%þicksim37.88% when achieving the same target accuracy compared to the compression-based baselines, while incurring only a 0.68% degradation in final test accuracy relative to the full-precision communication.Keywords
This publication has 26 references indexed in Scilit:
- Heroes: Lightweight Federated Learning with Neural Composition and Adaptive Local Update in Heterogeneous Edge Networks2024
- MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation2024
- Towards efficient communications in federated learning: A contemporary surveyJournal of the Franklin Institute, 2023
- Optimal Rate Adaption in Federated Learning with Compressed Communications2022
- Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling2022
- FedMP: Federated Learning through Adaptive Model Pruning in Heterogeneous Edge Computing2022
- To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices2021
- When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning2018
- Deep Residual Learning for Image Recognition2016
- Approximating the Kullback Leibler Divergence Between Gaussian Mixture Models2007