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Dynamic Federated Learning
[article]
2020
arXiv
pre-print
Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. While many federated learning architectures process data in an online manner, and are hence adaptive by nature, most performance analyses assume static optimization problems and offer no guarantees in the presence of drifts in the problem solution or data characteristics. We consider a federated learning model where at every iteration, a random subset of available agents
arXiv:2002.08782v2
fatcat:l5cvo2kynnf55lbbetrjsc7bxq