Federated Learning (FL) has garnered increasing attention due to its unique characteristic of allowing heterogeneous clients to process their private data locally and interact with a central server, while being respectful of privacy. A critical bottleneck in FL is the communication cost. A pivotal strategy to mitigate this burden is \emph{Local Training}, which involves running multiple local stochastic gradient descent iterations between communication phases. Our work is inspired by the innovative \emph{Scaffnew} algorithm, which has considerably advanced the reduction of communication complexity in FL. We introduce FedComLoc (Federated Compressed and Local Training), integrating practical and effective compression into \emph{Scaffnew} to further enhance communication efficiency. Extensive experiments, using the popular TopK compressor and quantization, demonstrate its prowess in substantially reducing communication overheads in heterogeneous settings.

翻译：联邦学习因其允许异构客户端在本地处理私有数据并与中央服务器交互、同时尊重隐私的独特特性而日益受到关注。联邦学习中的一个关键瓶颈是通信成本。缓解该负担的核心策略是**本地训练**，即在通信轮次之间执行多次本地随机梯度下降迭代。我们的研究受创新的*Scaffnew*算法启发，该算法显著推进了联邦学习中通信复杂度的降低。我们提出FedComLoc（联邦压缩与本地训练），将实用且高效的压缩技术集成到*Scaffnew*中，以进一步提升通信效率。通过使用流行的TopK压缩器和量化技术进行的大量实验表明，该方法在异构环境下显著减少通信开销方面展现出卓越性能。