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Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration
Time
Location
Event Type
Research Manuscript
Virtual Programs
Hosted in Virtual Platform
Keywords
AI/ML System Design
Topics
Design
DescriptionIn this paper, we propose Helios — a heterogeneity-aware FL framework to tackle the straggler issue. Helios identifies individual devices’ heterogeneous training capability, and therefore the expected neural network model training volumes regarding the collaborative training pace. For straggling devices, a “soft-training” method is proposed to dynamically compress the original identical training model into the expected volume through a rotating neuron training approach. With extensive algorithm analysis and optimization schemes, the stragglers can be accelerated while retaining the convergence for local training as well as federated collaboration.