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Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach
We consider a many-to-one wireless architecture for federated learning at the network edge, where multiple edge devices collaboratively train a model using local data.
Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence
Learning generative models is challenging for a network edge node with limited data and computing power.
Accelerating Distributed Online Meta-Learning via Multi-Agent Collaboration under Limited Communication
By characterizing the upper bound of the agent-task-averaged regret, we show that the performance of multi-agent online meta-learning depends heavily on how much an agent can benefit from the distributed network-level OCO for meta-model updates via limited communication, which however is not well understood.
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