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Found 5 papers, 0 papers with code
Communication Efficient and Provable Federated Unlearning
We start by giving a rigorous definition of \textit{exact} federated unlearning, which guarantees that the unlearned model is statistically indistinguishable from the one trained without the deleted data.
Resource-Adaptive Newton's Method for Distributed Learning
Distributed stochastic optimization methods based on Newton's method offer significant advantages over first-order methods by leveraging curvature information for improved performance.
Byzantine-Resilient Federated Learning at Edge
To address this issue, we study the stochastic convex and non-convex optimization problem for federated learning at edge and show how to handle heavy-tailed data while retaining the Byzantine resilience, communication efficiency and the optimal statistical error rates simultaneously.
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits
Finally, we establish the lower bound to show that the instance-dependent regret of our improved algorithm is optimal.
A Distributed Privacy-Preserving Learning Dynamics in General Social Networks
In this paper, we study a distributed privacy-preserving learning problem in social networks with general topology.
