Stochastic Controlled Averaging for Federated Learning with Communication Compression
This work addresses communication overhead in federated learning, a critical bottleneck for distributed AI systems, though it is incremental as it builds upon prior stochastic controlled averaging methods.
The paper tackles the challenge of communication compression in federated learning, which often suffers from performance limitations due to data heterogeneity and partial participation, by proposing two algorithms (SCALLION and SCAFCOM) that achieve performance matching full-precision approaches with substantially reduced uplink communication and outperform existing compressed methods under the same budget.
Communication compression, a technique aiming to reduce the information volume to be transmitted over the air, has gained great interests in Federated Learning (FL) for the potential of alleviating its communication overhead. However, communication compression brings forth new challenges in FL due to the interplay of compression-incurred information distortion and inherent characteristics of FL such as partial participation and data heterogeneity. Despite the recent development, the performance of compressed FL approaches has not been fully exploited. The existing approaches either cannot accommodate arbitrary data heterogeneity or partial participation, or require stringent conditions on compression. In this paper, we revisit the seminal stochastic controlled averaging method by proposing an equivalent but more efficient/simplified formulation with halved uplink communication costs. Building upon this implementation, we propose two compressed FL algorithms, SCALLION and SCAFCOM, to support unbiased and biased compression, respectively. Both the proposed methods outperform the existing compressed FL methods in terms of communication and computation complexities. Moreover, SCALLION and SCAFCOM accommodates arbitrary data heterogeneity and do not make any additional assumptions on compression errors. Experiments show that SCALLION and SCAFCOM can match the performance of corresponding full-precision FL approaches with substantially reduced uplink communication, and outperform recent compressed FL methods under the same communication budget.