Distributed Stochastic Multi-Task Learning with Graph Regularization
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Abstract (arXiv)
We propose methods for distributed graph-based multi-task learning that are based on weighted averaging of messages from other machines. Uniform averaging or diminishing stepsize in these methods would yield consensus (single task) learning. We show how simply skewing the averaging weights or controlling the stepsize allows learning different, but related, tasks on the different machines.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Feb 2018.
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Citing papers per year: 2021: 4; 2022: 1; 2023: 1.
6 papers.
- 2021Personalized Federated Learning: A Unified Framework and Universal Optimization Techniquescited by 34
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- 2023PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexitiescited by 5
- 2021Decentralized Personalized Federated Learning for Min-Max Problemscited by 4
- 2022Personalized Federated Learning with Multiple Known Clusterscited by 3
- 2023PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities5 Mar 2023
- 2022Personalized Federated Learning with Multiple Known Clusters28 Apr 2022
- 2021Decentralized Personalized Federated Learning for Min-Max Problems14 Jun 2021
- 2021GT-STORM: Taming Sample, Communication, and Memory Complexities in Decentralized Non-Convex Learning4 May 2021
- 2021Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques19 Feb 2021
- 2021An Adaptive Stochastic Sequential Quadratic Programming with Differentiable Exact Augmented Lagrangians10 Feb 2021