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Exact Subspace Diffusion for Decentralized Multitask Learning
Classical paradigms for distributed learning, such as federated or decentralized gradient descent, employ consensus mechanisms to enforce homogeneity among agents.
Quantization for decentralized learning under subspace constraints
In this paper, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces.
Dencentralized learning in the presence of low-rank noise
Observations collected by agents in a network may be unreliable due to observation noise or interference.
Multitask learning over graphs: An Approach for Distributed, Streaming Machine Learning
Multitask learning is an approach to inductive transfer learning (using what is learned for one problem to assist in another problem) and helps improve generalization performance relative to learning each task separately by using the domain information contained in the training signals of related tasks as an inductive bias.
Network Classifiers With Output Smoothing
This work introduces two strategies for training network classifiers with heterogeneous agents.
