Papers citing "Advances and Open Problems in Federated Learning"

50 / 193 papers shown

Title
Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models Jun Luo Chong Chen Shandong Wu FedML VLM MoE 75 3 0 14 Oct 2024
CollabEdit: Towards Non-destructive Collaborative Knowledge Editing Jiamu Zheng Jinghuai Zhang Tianyu Du Xuhong Zhang Jianwei Yin Tao Lin KELM 90 0 0 12 Oct 2024
Scalable and Resource-Efficient Second-Order Federated Learning via Over-the-Air Aggregation Abdulmomen Ghalkha Chaouki Ben Issaid Mehdi Bennis 45 0 0 10 Oct 2024
FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning Nurbek Tastan Samuel Horváth Martin Takáč Karthik Nandakumar FedML 86 0 0 03 Oct 2024
Selective Aggregation for Low-Rank Adaptation in Federated Learning Pengxin Guo Shuang Zeng Y. Wang Huijie Fan Feifei Wang Liangqiong Qu FedML 78 12 0 02 Oct 2024
Peer-to-Peer Learning Dynamics of Wide Neural Networks Shreyas Chaudhari Srinivasa Pranav Emile Anand José M. F. Moura 64 3 0 23 Sep 2024
Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework Zilinghan Li Shilan He Ze Yang Minseok Ryu Kibaek Kim Ravi K. Madduri FedML 84 10 0 17 Sep 2024
On the effects of similarity metrics in decentralized deep learning under distributional shift Edvin Listo Zec Tom Hagander Eric Ihre-Thomason Sarunas Girdzijauskas FedML 76 0 0 16 Sep 2024
Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity Hao Jian Huang Bekzod Iskandarov Mizanur Rahman FedML 91 1 0 15 Sep 2024
pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning Jiahao Lai Jiaqiang Li Jian Xu Yanru Wu Boshi Tang Siqi Chen Yongfeng Huang Wenbo Ding Yang Li FedML 107 0 0 09 Sep 2024
The Key of Parameter Skew in Federated Learning Sifan Wang Junfeng Liao Ye Yuan Riquan Zhang FedML 55 0 0 21 Aug 2024
A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs H. B. McMahan Zheng Xu Yanxiang Zhang FedML 81 7 0 16 Aug 2024
FedQUIT: On-Device Federated Unlearning via a Quasi-Competent Virtual Teacher Alessio Mora Lorenzo Valerio Paolo Bellavista A. Passarella FedML MU 73 2 0 14 Aug 2024
LiD-FL: Towards List-Decodable Federated Learning Hong Liu Liren Shan Han Bao Ronghui You Yuhao Yi Jiancheng Lv FedML 114 0 0 09 Aug 2024
Improved Bounds for Pure Private Agnostic Learning: Item-Level and User-Level Privacy Bo Li Wei Wang Peng Ye FedML 57 0 0 30 Jul 2024
Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization Zhe Li Bicheng Ying Zidong Liu Chaosheng Dong Haibo Yang FedML 90 3 0 24 May 2024
Distributed Event-Based Learning via ADMM Güner Dilsad Er Sebastian Trimpe Michael Muehlebach FedML 64 2 0 17 May 2024
Robust Decentralized Learning with Local Updates and Gradient Tracking Sajjad Ghiasvand Amirhossein Reisizadeh Mahnoosh Alizadeh Ramtin Pedarsani 72 4 0 02 May 2024
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis Guangchen Lan Dong-Jun Han Abolfazl Hashemi Vaneet Aggarwal Christopher G. Brinton 144 15 0 09 Apr 2024
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems Ayoub Si-Ahmed M. Al-garadi Boustia Narhimene 78 4 0 14 Mar 2024
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression Laurent Condat Artavazd Maranjyan Peter Richtárik 92 5 0 07 Mar 2024
ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning Kaan Ozkara Bruce Huang Ruida Zhou Suhas Diggavi 138 0 0 19 Feb 2024
Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach Mahrokh Ghoddousi Boroujeni Andreas Krause Giancarlo Ferrari-Trecate FedML 65 3 0 16 Jan 2024
FBChain: A Blockchain-based Federated Learning Model with Efficiency and Secure Communication Yang Li Chunhe Xia Wei Liu 84 0 0 21 Nov 2023
Multi-Agent Consensus Seeking via Large Language Models Huaben Chen Wenkang Ji Lufeng Xu Shiyu Zhao LM&Ro LLMAG 88 23 0 31 Oct 2023
Revolutionizing Wireless Networks with Federated Learning: A Comprehensive Review Sajjad Emdadi Mahdimahalleh AI4CE 58 0 0 01 Aug 2023
Compressed Private Aggregation for Scalable and Robust Federated Learning over Massive Networks Natalie Lang Nir Shlezinger Rafael G. L. DÓliveira S. E. Rouayheb FedML 102 4 0 01 Aug 2023
When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions Weiming Zhuang Chen Chen Lingjuan Lyu Chong Chen Yaochu Jin Lingjuan Lyu AIFin AI4CE 118 93 0 27 Jun 2023
FedNoisy: Federated Noisy Label Learning Benchmark Siqi Liang Jintao Huang Junyuan Hong Dun Zeng Jiayu Zhou Zenglin Xu FedML 69 7 0 20 Jun 2023
Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging Jiamian Wang Zong-Jhe Wu Yulun Zhang Xin Yuan Tao R. Lin Zhiqiang Tao FedML 88 3 0 01 Jun 2023
Incentivizing Honesty among Competitors in Collaborative Learning and Optimization Florian E. Dorner Nikola Konstantinov Georgi Pashaliev Martin Vechev FedML 54 6 0 25 May 2023
SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization Kfir Y. Levy Kfir Y. Levy FedML 63 3 0 09 Apr 2023
Graph Learning Across Data Silos Xiang Zhang Qiao Wang 111 1 0 17 Jan 2023
FedCC: Robust Federated Learning against Model Poisoning Attacks Hyejun Jeong Hamin Son Seohu Lee Jayun Hyun Tai-Myoung Chung FedML 88 6 0 05 Dec 2022
Enhancing Efficiency in Multidevice Federated Learning through Data Selection Fan Mo Mohammad Malekzadeh S. Chatterjee F. Kawsar Akhil Mathur FedML 56 2 0 08 Nov 2022
Communication-Efficient Federated Learning With Data and Client Heterogeneity Hossein Zakerinia Shayan Talaei Giorgi Nadiradze Dan Alistarh FedML 58 8 0 20 Jun 2022
(Im)possibility of Collective Intelligence Krikamol Muandet 161 6 0 05 Jun 2022
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms Pranay Sharma Rohan Panda Gauri Joshi P. Varshney FedML 66 49 0 09 Mar 2022
Training Differentially Private Models with Secure Multiparty Computation Sikha Pentyala Davis Railsback Ricardo Maia Rafael Dowsley David Melanson Anderson C. A. Nascimento Martine De Cock 33 14 0 05 Feb 2022
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback Boxin Zhao Lingxiao Wang Mladen Kolar Ziqi Liu Qing Cui Jun Zhou Chaochao Chen FedML 98 11 0 28 Dec 2021
An Operator Splitting View of Federated Learning Saber Malekmohammadi Kiarash Shaloudegi Zeou Hu Yaoliang Yu FedML 61 2 0 12 Aug 2021
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 406 1,868 0 14 Dec 2020
Training Production Language Models without Memorizing User Data Swaroop Indra Ramaswamy Om Thakkar Rajiv Mathews Galen Andrew H. B. McMahan Franccoise Beaufays FedML 44 92 0 21 Sep 2020
NoPeek: Information leakage reduction to share activations in distributed deep learning Praneeth Vepakomma Abhishek Singh O. Gupta Ramesh Raskar MIACV FedML 46 84 0 20 Aug 2020
Shuffled Model of Federated Learning: Privacy, Communication and Accuracy Trade-offs Antonious M. Girgis Deepesh Data Suhas Diggavi Peter Kairouz A. Suresh FedML 56 25 0 17 Aug 2020
Dispersed Federated Learning: Vision, Taxonomy, and Future Directions L. U. Khan Walid Saad Zhu Han Choong Seon Hong 80 33 0 12 Aug 2020
Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning Sai Praneeth Karimireddy Martin Jaggi Satyen Kale M. Mohri Sashank J. Reddi Sebastian U. Stich A. Suresh FedML 66 217 0 08 Aug 2020
SplitNN-driven Vertical Partitioning Iker Ceballos Vivek Sharma Eduardo Mugica Abhishek Singh Alberto Roman Praneeth Vepakomma Ramesh Raskar 39 73 0 07 Aug 2020
Flower: A Friendly Federated Learning Research Framework Daniel J. Beutel Taner Topal Akhil Mathur Xinchi Qiu Javier Fernandez-Marques ... Lorenzo Sani Kwing Hei Li Titouan Parcollet Pedro Porto Buarque de Gusmão Nicholas D. Lane FedML 64 795 0 28 Jul 2020
Tempered Sigmoid Activations for Deep Learning with Differential Privacy Nicolas Papernot Abhradeep Thakurta Shuang Song Steve Chien Ulfar Erlingsson AAML 172 179 0 28 Jul 2020