AISTATS

2023

• Characterizing Internal Evasion Attacks in Federated Learning.

• Byzantine-Robust Federated Learning with Optimal Statistical Rates.

• The communication cost of security and privacy in federated frequency estimation.

• Federated Averaging Langevin Dynamics: Toward a unified theory of and new algorithms.

• Active Membership Inference Attack under Local Differential Privacy in Federated Learning.

• Private Non-Convex Federated Learning Without a Trusted Server.

• Federated Learning under Distributed Concept Drift.

• Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout.

• Nothing but Regrets --- Privacy-Preserving Federated Causal Discovery.

• Federated Learning for Data Streams.

• Federated Asymptotics: a model to compare federated learning algorithms.