On Consensus-Optimality Trade-offs in Collaborative Deep Learning
This work addresses the challenge of balancing agreement and performance for agents with private data in distributed machine learning, representing an incremental advancement.
The paper tackles the trade-off between consensus and optimality in distributed deep learning by proposing two algorithms, i-CDSGD and g-CDSGD, which enable control over this spectrum and show significant improvements over existing methods in numerical experiments.
In distributed machine learning, where agents collaboratively learn from diverse private data sets, there is a fundamental tension between consensus and optimality. In this paper, we build on recent algorithmic progresses in distributed deep learning to explore various consensus-optimality trade-offs over a fixed communication topology. First, we propose the incremental consensus-based distributed SGD (i-CDSGD) algorithm, which involves multiple consensus steps (where each agent communicates information with its neighbors) within each SGD iteration. Second, we propose the generalized consensus-based distributed SGD (g-CDSGD) algorithm that enables us to navigate the full spectrum from complete consensus (all agents agree) to complete disagreement (each agent converges to individual model parameters). We analytically establish convergence of the proposed algorithms for strongly convex and nonconvex objective functions; we also analyze the momentum variants of the algorithms for the strongly convex case. We support our algorithms via numerical experiments, and demonstrate significant improvements over existing methods for collaborative deep learning.