"distributed machine learning" Papers
12 papers found
Conference
A Fair Federated Learning Method for Handling Client Participation Probability Inconsistencies in Heterogeneous Environments
Siyuan Wu, Yongzhe Jia, Haolong Xiang et al.
NEURIPS 2025
Competitive Advantage Attacks to Decentralized Federated Learning
Yuqi Jia, Minghong Fang, Neil Gong
NEURIPS 2025arXiv:2310.13862
1
citations
Decentralized Optimization with Coupled Constraints
Demyan Yarmoshik, Alexander Rogozin, Nikita Kiselev et al.
ICLR 2025arXiv:2407.02020
2
citations
FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors
Changlong Shi, He Zhao, Bingjie Zhang et al.
CVPR 2025arXiv:2503.15842
7
citations
Multiplayer Federated Learning: Reaching Equilibrium with Less Communication
TaeHo Yoon, Sayantan Choudhury, Nicolas Loizou
NEURIPS 2025arXiv:2501.08263
5
citations
On the Robustness of Distributed Machine Learning Against Transfer Attacks
Sebastien Andreina, Pascal Zimmer, Ghassan Karame
AAAI 2025paperarXiv:2412.14080
1
citations
Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers
Ron Dorfman, Naseem Yehya, Kfir Levy
ICML 2024arXiv:2402.02951
5
citations
Fault Tolerant ML: Efficient Meta-Aggregation and Synchronous Training
Tehila Dahan, Kfir Levy
ICML 2024arXiv:2405.14759
3
citations
FedBAT: Communication-Efficient Federated Learning via Learnable Binarization
Shiwei Li, Wenchao Xu, Haozhao Wang et al.
ICML 2024arXiv:2408.03215
12
citations
Implicit Regularization in Feedback Alignment Learning Mechanisms for Neural Networks
Zach Robertson, Sanmi Koyejo
ICML 2024arXiv:2306.01870
Position: Exploring the Robustness of Pipeline-Parallelism-Based Decentralized Training
Lin Lu, Chenxi Dai, Wangcheng Tao et al.
ICML 2024
Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective
Zhen Qin, Feiyi Chen, Chen Zhi et al.
AAAI 2024paperarXiv:2309.16456
18
citations