Convergence-Driven Federated Learning with Joint Compression and Computation Optimization

Federated Learning (FL) has emerged as a powerful paradigm for distributed model training that preserves data privacy while aggregating model updates through a central server. However, efficiently implementing federated learning in resource-constrained and heterogeneous edge environments remains challenging due to the trade-off between convergence performance and computation/communication costs. Most of the existing approaches only heuristically tune the compression and computation parameters without directly minimizing the theoretical convergence bound. In this paper, we propose the first FL framework, Convergence-driven Federated learning (ConFed), that explicitly formulates and minimizes the non-convex convergence error upper bound under time-averaged expected resource constraints, providing a principled foundation for joint optimization of computation and compression. ConFed supports diverse compression mechanisms, including Top-k sparsification, random quantization, and their combinations. Extensive experiments on Fashion-MNIST (FMNIST) and CIFAR-10 demonstrate that our method achieves faster convergence and lower resource costs compared to state-of-the-art approaches.