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Found 5 papers, 4 papers with code
SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization
Machine Unlearning (MU) is an increasingly important topic in machine learning safety, aiming at removing the contribution of a given data point from a training procedure.
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates
We show that our general framework applies to existing optimization schemes including centralized learning, FedAvg, asynchronous FedAvg, and FedBuff.
A General Theory for Client Sampling in Federated Learning
In this work, we provide a general theoretical framework to quantify the impact of a client sampling scheme and of the clients heterogeneity on the federated optimization.
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning
This work addresses the problem of optimizing communications between server and clients in federated learning (FL).
Free-rider Attacks on Model Aggregation in Federated Learning
Free-rider attacks against federated learning consist in dissimulating participation to the federated learning process with the goal of obtaining the final aggregated model without actually contributing with any data.
