Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer

24 December 2019

Papers citing "Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer"

33 / 33 papers shown

Title
Learning Critically: Selective Self Distillation in Federated Learning on Non-IID Data Yuting He Yiqiang Chen Xiaodong Yang H. Yu Yi-Hua Huang Yang Gu FedML 55 20 0 20 Apr 2025
Corrected with the Latest Version: Make Robust Asynchronous Federated Learning Possible Chaoyi Lu Yiding Sun Pengbo Li Zhichuan Yang FedML 34 0 0 05 Apr 2025
Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models Y. Cai Ziqi Zhang Ding Li Yao Guo Xiangqun Chen 48 0 0 13 Mar 2025
Robust Asymmetric Heterogeneous Federated Learning with Corrupted Clients Xiuwen Fang Mang Ye Bo Du FedML 66 1 0 12 Mar 2025
Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Won-Jun Jang Hyeon-Seo Park Si-Hyeon Lee FedML 155 0 0 10 Feb 2025
Decoding FL Defenses: Systemization, Pitfalls, and Remedies M. A. Khan Virat Shejwalkar Yasra Chandio Amir Houmansadr Fatima M. Anwar AAML 38 0 0 03 Feb 2025
CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning Nurbek Tastan Samuel Horváth Karthik Nandakumar FedML 69 0 0 21 Jan 2025
Practical Insights into Knowledge Distillation for Pre-Trained Models Norah Alballa Marco Canini 37 2 0 22 Feb 2024
On the Byzantine-Resilience of Distillation-Based Federated Learning Christophe Roux Max Zimmer S. Pokutta AAML 49 1 0 19 Feb 2024
FedImpro: Measuring and Improving Client Update in Federated Learning Zhenheng Tang Yonggang Zhang S. Shi Xinmei Tian Tongliang Liu Bo Han Xiaowen Chu FedML 17 13 0 10 Feb 2024
Teacher-Student Architecture for Knowledge Distillation: A Survey Chengming Hu Xuan Li Danyang Liu Haolun Wu Xi Chen Ju Wang Xue Liu 21 16 0 08 Aug 2023
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency Jiawei Shao Zijian Li Wenqiang Sun Tailin Zhou Yuchang Sun Lumin Liu Zehong Lin Yuyi Mao Jun Zhang FedML 30 23 0 20 Jul 2023
Heterogeneous Federated Learning: State-of-the-art and Research Challenges Mang Ye Xiuwen Fang Bo Du PongChi Yuen Dacheng Tao FedML AAML 33 244 0 20 Jul 2023
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy Yifei Zhang Dun Zeng Jinglong Luo Zenglin Xu Irwin King FedML 76 47 0 21 Feb 2023
FedICT: Federated Multi-task Distillation for Multi-access Edge Computing Zhiyuan Wu Sheng Sun Yuwei Wang Min Liu Quyang Pan Xue Jiang Bo Gao 35 31 0 01 Jan 2023
Security Analysis of SplitFed Learning M. A. Khan Virat Shejwalkar Amir Houmansadr Fatima M. Anwar FedML 8 11 0 04 Dec 2022
Scalable Collaborative Learning via Representation Sharing Frédéric Berdoz Abhishek Singh Martin Jaggi Ramesh Raskar FedML 19 3 0 20 Nov 2022
The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning Virat Shejwalkar Lingjuan Lyu Amir Houmansadr AAML 25 10 0 01 Nov 2022
Federated Learning with Privacy-Preserving Ensemble Attention Distillation Xuan Gong Liangchen Song Rishi Vedula Abhishek Sharma Meng Zheng ... Arun Innanje Terrence Chen Junsong Yuan David Doermann Ziyan Wu FedML 15 27 0 16 Oct 2022
Label driven Knowledge Distillation for Federated Learning with non-IID Data Minh-Duong Nguyen Viet Quoc Pham D. Hoang Long Tran-Thanh Diep N. Nguyen W. Hwang 16 2 0 29 Sep 2022
Towards Efficient Communications in Federated Learning: A Contemporary Survey Zihao Zhao Yuzhu Mao Yang Liu Linqi Song Ouyang Ye Xinlei Chen Wenbo Ding FedML 49 59 0 02 Aug 2022
Enhanced Security and Privacy via Fragmented Federated Learning N. Jebreel J. Domingo-Ferrer Alberto Blanco-Justicia David Sánchez FedML 13 26 0 13 Jul 2022
Defending against the Label-flipping Attack in Federated Learning N. Jebreel J. Domingo-Ferrer David Sánchez Alberto Blanco-Justicia AAML 13 36 0 05 Jul 2022
FL-Defender: Combating Targeted Attacks in Federated Learning N. Jebreel J. Domingo-Ferrer AAML FedML 43 56 0 02 Jul 2022
Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity Kiwan Maeng Haiyu Lu Luca Melis John Nguyen Michael G. Rabbat Carole-Jean Wu FedML 29 31 0 30 May 2022
DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems Martin Rapp R. Khalili Kilian Pfeiffer J. Henkel 19 18 0 16 Dec 2021
Membership Inference Attacks on Machine Learning: A Survey Hongsheng Hu Z. Salcic Lichao Sun Gillian Dobbie Philip S. Yu Xuyun Zhang MIACV 30 412 0 14 Mar 2021
Privacy and Robustness in Federated Learning: Attacks and Defenses Lingjuan Lyu Han Yu Xingjun Ma Chen Chen Lichao Sun Jun Zhao Qiang Yang Philip S. Yu FedML 175 355 0 07 Dec 2020
Practical One-Shot Federated Learning for Cross-Silo Setting Qinbin Li Bingsheng He D. Song FedML 11 113 0 02 Oct 2020
Federated Model Distillation with Noise-Free Differential Privacy Lichao Sun Lingjuan Lyu FedML 11 106 0 11 Sep 2020
An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies David Enthoven Zaid Al-Ars FedML 54 50 0 01 Apr 2020
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 177 1,032 0 29 Nov 2018
Large scale distributed neural network training through online distillation Rohan Anil Gabriel Pereyra Alexandre Passos Róbert Ormándi George E. Dahl Geoffrey E. Hinton FedML 267 404 0 09 Apr 2018