Federated Learning Algorithm

Federated learning (FL) is a distributed machine learning approach that trains models on decentralized data without directly sharing sensitive information. Current research focuses on improving the robustness and efficiency of FL algorithms like FedAvg and FedProx, particularly in addressing data heterogeneity across participating devices and mitigating the impact of uneven client participation. This is achieved through techniques such as variance reduction, adaptive learning rates, and novel aggregation strategies, with a growing emphasis on balancing privacy preservation with model accuracy. The impact of FL extends to various applications, including healthcare and IoT, where data privacy is paramount.

Papers

August 11, 2024

On the Convergence of a Federated Expectation-Maximization Algorithm
Zhixu Tao, Rajita Chandak, Sanjeev Kulkarni
Early Stage Convergence Convergence Rate Expectation Maximization Federated Learning Algorithm Federated Automatic Differentiation

May 22, 2024

Fair Evaluation of Federated Learning Algorithms for Automated Breast Density Classification: The Results of the 2022 ACR-NCI-NVIDIA Federated Learning Challenge
Kendall Schmidt, Benjamin Bearce, Ken Chang, Laura Coombs, Keyvan Farahani, Marawan Elbatele, Kaouther Mouhebe, Robert Marti, Ruipeng Zhang, Yao Zhang, Yanfeng Wang, Yaojun Hu, Haochao Ying, Yuyang Xu, Conrad Testagrose, Mutlu Demirer, Vikash Gupta, Ünal Akünal, Markus Bujotzek, Klaus H. Maier-Hein, Yi Qin, Xiaomeng Li, Jayashree Kalpathy-Cramer, Holger R. Roth
Federated Learning Key Result Realistic Evaluation Digital Mammogram Federated Learning Algorithm Breast Density

May 7, 2024

FedStale: leveraging stale client updates in federated learning
Angelo Rodio, Giovanni Neglia
Federated Learning Algorithm Stale Gradient

March 26, 2024

Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study
Gustav A. Baumgart, Jaemin Shin, Ali Payani, Myungjin Lee, Ramana Rao Kompella
Federated Learning Performance Evaluation Federated Learning Algorithm Open Source Federated Learning Framework

February 29, 2024

On the Convergence of Federated Learning Algorithms without Data Similarity
Ali Beikmohammadi, Sarit Khirirat, Sindri Magnússon
Federated Learning Early Stage Convergence Slow Convergence Convergent Algorithm Federated Learning Algorithm Data Similarity

February 15, 2024

Investigation of Federated Learning Algorithms for Retinal Optical Coherence Tomography Image Classification with Statistical Heterogeneity
Sanskar Amgain, Prashant Shrestha, Sophia Bano, Ignacio del Valle Torres, Michael Cunniffe, Victor Hernandez, Phil Beales, Binod Bhattarai
Optical Coherence Tomography Comprehensive Investigation Statistical Heterogeneity Federated Learning Algorithm

December 24, 2023

An Empirical Study of Efficiency and Privacy of Federated Learning Algorithms
Sofia Zahri, Hajar Bennouri, Ahmed M. Abdelmoniem
High Efficiency Differential Privacy Empirical Study Privacy Policy Secure Aggregation Internet of Thing Network Privacy Preserving Machine Learning Federated Learning Algorithm

September 13, 2023

Learning From Drift: Federated Learning on Non-IID Data via Drift Regularization
Yeachan Kim, Bonggun Shin
Strong Generalization Raw Data Real Drift Federated Learning Algorithm Drift Plus Penalty

March 31, 2023

Benchmarking FedAvg and FedCurv for Image Classification Tasks
Bruno Casella, Roberto Esposito, Carlo Cavazzoni, Marco Aldinucci
Image Classification Task FedAvg Algorithm Data Lake Federated Learning Algorithm

February 10, 2023

XFL: A High Performace, Lightweighted Federated Learning Framework
Hong Wang, Yuanzhi Zhou, Chi Zhang, Chen Peng, Mingxia Huang, Yi Liu, Lintao Zhang
Homomorphic Encryption Federated Setting Secure Multi Party Computation Federated Learning Algorithm

December 14, 2022

FLAGS Framework for Comparative Analysis of Federated Learning Algorithms
Ahnaf Hannan Lodhi, Barış Akgün, Öznur Özkasap
Comparative Study Federated Averaging Decentralized Federated Learning Federated Learning Algorithm Gossip Learning

December 3, 2022

Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework
Shuai Wang, Yanqing Xu, Zhiguo Wang, Tsung-Hui Chang, Tony Q. S. Quek, Defeng Sun
Primal Dual Alternating Direction Method of Multiplier Federated Learning Algorithm Client Variance Reduction

December 2, 2022

Faster Adaptive Federated Learning
Xidong Wu, Feihu Huang, Zhengmian Hu, Heng Huang
Adaptive Algorithm Federated Learning Algorithm FL Algorithm Adaptive Federated Learning

June 6, 2022

Pretrained Models for Multilingual Federated Learning
Orion Weller, Marc Marone, Vladimir Braverman, Dawn Lawrie, Benjamin Van Durme
Natural Language Processing Multilingual Task Multilingual Text Federated Learning Algorithm Federated Large Language Model

June 2, 2022

Federated Learning with a Sampling Algorithm under Isoperimetry
Lukang Sun, Adil Salim, Peter Richtárik
Langevin Dynamic Communication Efficient Sampling Based Algorithm Federated Learning Algorithm Log Sobolev

May 23, 2022

FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation
Tobias Bernecker, Annette Peters, Christopher L. Schlett, Fabian Bamberg, Fabian Theis, Daniel Rueckert, Jakob Weiß, Shadi Albarqouni
Normalization Dictionary Liver Segmentation Federated Learning Algorithm

February 10, 2022

Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning
Alberto Bietti, Chen-Yu Wei, Miroslav Dudík, John Langford, Zhiwei Steven Wu
Differential Privacy Generalization Guarantee Centralized Learning Private Model Privacy Accuracy Federated Learning Algorithm