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Self-Duplicating Random Walks for Resilient Decentralized Learning on Graphs
We propose a decentralized algorithm called DECAFORK that can maintain the number of RWs in the graph around a desired value even in the presence of arbitrary RW failures.
Walk for Learning: A Random Walk Approach for Federated Learning from Heterogeneous Data
One of the main challenges of FL is the communication bottleneck between the nodes and the parameter server.
Private Weighted Random Walk Stochastic Gradient Descent
To speed up the convergence, we propose instead to study random walk based SGD in which a global model is updated based on a random walk on the graph.
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