70 research outputs found
One-Bit Byzantine-Tolerant Distributed Learning via Over-the-Air Computation
Distributed learning has become a promising computational parallelism
paradigm that enables a wide scope of intelligent applications from the
Internet of Things (IoT) to autonomous driving and the healthcare industry.
This paper studies distributed learning in wireless data center networks, which
contain a central edge server and multiple edge workers to collaboratively
train a shared global model and benefit from parallel computing. However, the
distributed nature causes the vulnerability of the learning process to faults
and adversarial attacks from Byzantine edge workers, as well as the severe
communication and computation overhead induced by the periodical information
exchange process. To achieve fast and reliable model aggregation in the
presence of Byzantine attacks, we develop a signed stochastic gradient descent
(SignSGD)-based Hierarchical Vote framework via over-the-air computation
(AirComp), where one voting process is performed locally at the wireless edge
by taking advantage of Bernoulli coding while the other is operated
over-the-air at the central edge server by utilizing the waveform superposition
property of the multiple-access channels. We comprehensively analyze the
proposed framework on the impacts including Byzantine attacks and the wireless
environment (channel fading and receiver noise), followed by characterizing the
convergence behavior under non-convex settings. Simulation results validate our
theoretical achievements and demonstrate the robustness of our proposed
framework in the presence of Byzantine attacks and receiver noise.Comment: This work has been submitted to the IEEE for possible publication.
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