双向压缩+本地训练，显著降低联邦学习通信开销
BiCoLoR: Communication-Efficient Optimization with Bidirectional Compression and Local Training
- 同时压缩上行和下行通信数据，减少传输量
- 在凸与强凸非均匀场景下，收敛速度更快
- 适合资源受限的无线联邦学习场景
分布式优化中通信缓慢且成本高昂是主要瓶颈，尤其在联邦学习中通过无线网络传输时更为明显。本文提出 BiCoLoR，一种通信高效的优化算法，结合了本地训练（增加通信间隔间的计算量）与压缩（将高维向量编码为短比特流）两种常用且有效的方法。尽管此前已有两者结合的尝试，但压缩通常仅应用于上行通信（客户端到服务器），而下行通信（服务器到客户端）仍无压缩。实际上，两个方向的通信都成本高昂。BiCoLoR 是首个将本地训练与任意无偏压缩器结合实现双向压缩的算法。该联合设计在凸与强凸异构设置下均获得加速的复杂度保证。实验表明，BiCoLoR 优于现有算法，在通信效率方面树立了新标准。
原文摘要 · Abstract (English)
Slow and costly communication is often the main bottleneck in distributed optimization, especially in federated learning where it occurs over wireless networks. We introduce BiCoLoR, a communication-efficient optimization algorithm that combines two widely used and effective strategies: local training, which increases computation between communication rounds, and compression, which encodes high-dimensional vectors into short bitstreams. While these mechanisms have been combined before, compression has typically been applied only to uplink (client-to-server) communication, leaving the downlink (server-to-client) side unaddressed. In practice, however, both directions are costly. We propose BiCoLoR, the first algorithm to combine local training with bidirectional compression using arbitrary unbiased compressors. This joint design achieves accelerated complexity guarantees in both convex and strongly convex heterogeneous settings. Empirically, BiCoLoR outperforms existing algorithms and establishes a new standard in communication efficiency.
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