As a promising distributed learning technology, analog aggregation based federated learning over the air (FLOA) provides high communication efficiency and privacy provisioning under the edge computing paradigm. When all edge devices (workers) simultaneously upload their local updates to the parameter server (PS) through commonly shared time-frequency resources, the PS obtains the averaged update only rather than the individual local ones. While such a concurrent transmission and aggregation scheme reduces the latency and communication costs, it unfortunately renders FLOA vulnerable to Byzantine attacks. Aiming at Byzantine-resilient FLOA, this paper starts from analyzing the channel inversion (CI) mechanism that is widely used for power control in FLOA. Our theoretical analysis indicates that although CI can achieve good learning performance in the benign scenarios, it fails to work well with limited defensive capability against Byzantine attacks. Then, we propose a novel scheme called the best effort voting (BEV) power control policy that is integrated with stochastic gradient descent (SGD). Our BEV-SGD enhances the robustness of FLOA to Byzantine attacks, by allowing all the workers to send their local updates at their maximum transmit power. Under worst-case attacks, we derive the expected convergence rates of FLOA with CI and BEV power control policies, respectively. The rate comparison reveals that our BEV-SGD outperforms its counterpart with CI in terms of better convergence behavior, which is verified by experimental simulations.
翻译:作为有希望的分布式学习技术,基于模拟聚合的航空联合学习(FLOA)在边缘计算范式下提供了较高的通信效率和隐私。当所有边缘装置(工人)通过共同共享的时间频率资源同时将其本地更新上传到参数服务器(PS)时,PS只获得平均更新,而不是单个本地更新。虽然这种同时传输和汇总计划降低了悬浮和通信成本,但不幸的是,它使FLOA易受拜占庭式攻击的影响。针对Byzantine-Relient FLOA,本文从分析广泛用于FLOA中控制电力的频道反向机制(CI)开始。我们的理论分析表明,虽然CI公司在良性情景下可以取得良好的学习成绩,但在对付Byzantine袭击的防御能力有限的情况下却不能很好地运作。随后,我们提出了一个称为最佳努力投票(BEV)权力控制政策,与Stochatic Tele(SGD)相融合。我们的BEV-SGD加强了他们向比尚攻击的频道的稳健性,让我们能够分别以最强的CRevA水平上显示其最强的对比的比。