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20242026
most citedFederated Learning Resilient to Byzantine Attacks and Data Heterogeneity

1 citations · 3 across the 20 of their papers we have counts for

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cs.CR2025

Dynamic Jailbreaking Attack

Kedong Xiu, Yunhan Yang, Churui Zeng +6

Existing gradient-based jailbreak attacks typically optimize a fixed-length adversarial suffix toward a predefined target response with a static optimization strategy. However, thi…

cs.LG2025

H+: An Efficient Similarity-Aware Aggregation for Byzantine Resilient Federated Learning

Shiyuan Zuo, Rongfei Fan, Cheng Zhan +3

Federated Learning (FL) enables decentralized model training without sharing raw data. However, it remains vulnerable to Byzantine attacks, which can compromise the aggregation of…

cs.MA2025

Effective Policy Learning for Multi-Agent Online Coordination Beyond Submodular Objectives

Qixin Zhang, Yan Sun, Can Jin +5

In this paper, we present two effective policy learning algorithms for multi-agent online coordination(MA-OC) problem. The first one, \texttt{MA-SPL}, not only can achieve the opti…

cs.LG2025

Consistent Estimation of Numerical Distributions under Local Differential Privacy by Wavelet Expansion

Puning Zhao, Zhikun Zhang, Bo Sun +4

Distribution estimation under local differential privacy (LDP) is a fundamental and challenging task. Significant progresses have been made on categorical data. However, due to dif…

cs.LG2025

On Theoretical Limits of Learning with Label Differential Privacy

Puning Zhao, Chuan Ma, Li Shen +2

Label differential privacy (DP) is designed for learning problems involving private labels and public features. While various methods have been proposed for learning under label DP…