HADFL: Heterogeneity-aware Decentralized Federated Learning Framework
This addresses efficiency and scalability issues for federated learning systems, particularly in scenarios with heterogeneous devices, though it is incremental as it builds on existing decentralized and asynchronous methods.
The paper tackles the problem of communication pressure and model generalization in federated learning by proposing HADFL, a decentralized asynchronous framework for heterogeneous devices, achieving speedups of up to 3.15x over decentralized-FedAvg and 4.68x over PyTorch distributed training with minimal accuracy loss.
Federated learning (FL) supports training models on geographically distributed devices. However, traditional FL systems adopt a centralized synchronous strategy, putting high communication pressure and model generalization challenge. Existing optimizations on FL either fail to speedup training on heterogeneous devices or suffer from poor communication efficiency. In this paper, we propose HADFL, a framework that supports decentralized asynchronous training on heterogeneous devices. The devices train model locally with heterogeneity-aware local steps using local data. In each aggregation cycle, they are selected based on probability to perform model synchronization and aggregation. Compared with the traditional FL system, HADFL can relieve the central server's communication pressure, efficiently utilize heterogeneous computing power, and can achieve a maximum speedup of 3.15x than decentralized-FedAvg and 4.68x than Pytorch distributed training scheme, respectively, with almost no loss of convergence accuracy.