Federated Stochastic

Federated stochastic methods aim to collaboratively train machine learning models across decentralized devices without directly sharing sensitive data. Current research focuses on improving the efficiency and robustness of algorithms like Federated Averaging (FedAvg) and Federated Stochastic Gradient Descent (FedSGD), addressing challenges posed by data heterogeneity and Byzantine attacks through techniques such as gradient scaling, normalization, and dynamic resource allocation. These advancements are significant for privacy-preserving machine learning in diverse applications like medical image analysis and IoT data processing, enabling collaborative model training while maintaining data security and integrity.

Papers

November 2, 2024

Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing
Fardin Jalil Piran, Zhiling Chen, Mohsen Imani, Farhad Imani
Hyperdimensional Computing Federated Averaging Federated Stochastic

October 30, 2024

Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents
Safwan Labbi, Daniil Tiapkin, Lorenzo Mancini, Paul Mangold, Eric Moulines
Single Agent Heterogeneous Agent UCB Algorithm Iterative Algorithm Multi Agent Setting Federated Stochastic

September 8, 2024

DynamicFL: Federated Learning with Dynamic Communication Resource Allocation
Qi Le, Enmao Diao, Xinran Wang, Vahid Tarokh, Jie Ding, Ali Anwar
Federated Averaging Statistical Heterogeneity Federated Stochastic Communication Heterogeneity

August 21, 2024

FedGS: Federated Gradient Scaling for Heterogeneous Medical Image Segmentation
Philip Schutte, Valentina Corbetta, Regina Beets-Tan, Wilson Silva
Medical Image Segmentation Segmentation Performance Disentangled Representation Federated Stochastic Heterogeneous Medical Image

August 18, 2024

Byzantine-resilient Federated Learning Employing Normalized Gradients on Non-IID Datasets
Shiyuan Zuo, Xingrun Yan, Rongfei Fan, Li Shen, Puning Zhao, Jie Xu, Han Hu
Data Set Non Convex Loss Function Byzantine Robust Federated Learning Federated Learning Approach Optimality Gap Federated Stochastic Byzantine Robust Method

August 10, 2024

Federated Smoothing Proximal Gradient for Quantile Regression with Non-Convex Penalties
Reza Mirzaeifard, Diyako Ghaderyan, Stefan Werner
Proximal Gradient Federated Stochastic Non Convex Penalty Minimax Concave Penalty Sparse Penalty

July 17, 2024

Comparing Federated Stochastic Gradient Descent and Federated Averaging for Predicting Hospital Length of Stay
Mehmet Yigit Balik
Stochastic Gradient Descent Model Training Federated Averaging Local Model Federated Stochastic

June 16, 2024

Federated Learning Optimization: A Comparative Study of Data and Model Exchange Strategies in Dynamic Networks
Alka Luqman, Yeow Wei Liang Brandon, Anupam Chattopadhyay
Synthetic Data Raw Data Comparative Study Knowledge Transfer Dynamic Network Federated Stochastic Optimal Data Model Exchange

June 10, 2024

Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Tests
T. Tony Cai, Abhinav Chakraborty, Lasse Vuursteen
Differential Privacy Optimal Rate Federated Stochastic DP Algorithm Drift Diffusion Goodness of Fit Differential Privacy Constraint Adaptive Testing

January 24, 2024

On Principled Local Optimization Methods for Federated Learning
Honglin Yuan
Federated Averaging Federated Optimization Local Optimization Federated Stochastic

November 2, 2023

Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization
Wei Shen, Minhui Huang, Jiawei Zhang, Cong Shen
Minimax Optimization Gradient Descent Ascent Federated Stochastic Federated Minimax Optimization

October 15, 2023

Federated Multi-Objective Learning
Haibo Yang, Zhuqing Liu, Jia Liu, Chaosheng Dong, Michinari Momma
Multi Objective Optimization Federated Prompt Cooperation Multi Agent Learning Multi Objective Learning Federated Stochastic

March 31, 2023

A Surface-Based Federated Chow Test Model for Integrating APOE Status, Tau Deposition Measure, and Hippocampal Surface Morphometry
Jianfeng Wu, Yi Su, Yanxi Chen, Wenhui Zhu, Eric M. Reiman, Richard J. Caselli, Kewei Chen, Paul M. Thompson, Junwen Wang, Yalin Wang
Federated Stochastic Morphological Profiling Tau Protein Hippocampal Shape

February 21, 2023

FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning
Anran Li, Hongyi Peng, Lan Zhang, Jiahui Huang, Qing Guo, Han Yu, Yang Liu
Feature Selection Homomorphic Encryption Vertical Federated Learning Feature Importance Gaussian Model Federated Stochastic Non Private

February 10, 2023

Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting
Enyi Jiang, Yibo Jacky Zhang, Sanmi Koyejo
Domain Shift Aggregation Method Gradient Projection Federated Stochastic Federated Domain Adaptation Linear Weighting Server Side Aggregation

November 3, 2022

Federated Hypergradient Descent
Andrew K Kan
Hyperparameter Optimization Hyperparameter Tuning Federated Averaging Federated Stochastic Federated Hyperparameter

September 16, 2022

Federated Coordinate Descent for Privacy-Preserving Multiparty Linear Regression
Xinlin Leng, Chenxu Li, Weifeng Xu, Yuyan Sun, Hongtao Wang
Gradient Descent Secure Aggregation Multi Party Private Estimation Federated Stochastic Greedy Coordinate

April 26, 2022

Federated Stochastic Primal-dual Learning with Differential Privacy
Yiwei Li, Shuai Wang, Tsung-Hui Chang, Chong-Yung Chi
Differential Privacy Primal Dual Primal Dual Algorithm Local SGD Federated Stochastic SGD Step

February 17, 2022

Federated Stochastic Gradient Descent Begets Self-Induced Momentum
Howard H. Yang, Zuozhu Liu, Yaru Fu, Tony Q. S. Quek, H. Vincent Poor
Stochastic Gradient Descent Mobile Edge Federated Stochastic

January 6, 2022

Federated Optimization of Smooth Loss Functions
Ali Jadbabaie, Anuran Makur, Devavrat Shah
Empirical Risk Minimization Federated Averaging Federated Optimization Federated Learning Framework Federated Stochastic Smooth Loss Function

Federated Stochastic

Papers

Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing

Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents

DynamicFL: Federated Learning with Dynamic Communication Resource Allocation

FedGS: Federated Gradient Scaling for Heterogeneous Medical Image Segmentation

Byzantine-resilient Federated Learning Employing Normalized Gradients on Non-IID Datasets

Federated Smoothing Proximal Gradient for Quantile Regression with Non-Convex Penalties

Comparing Federated Stochastic Gradient Descent and Federated Averaging for Predicting Hospital Length of Stay

Federated Learning Optimization: A Comparative Study of Data and Model Exchange Strategies in Dynamic Networks

Federated Nonparametric Hypothesis Testing with Differential Privacy Constraints: Optimal Rates and Adaptive Tests

On Principled Local Optimization Methods for Federated Learning

Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization

Federated Multi-Objective Learning

A Surface-Based Federated Chow Test Model for Integrating APOE Status, Tau Deposition Measure, and Hippocampal Surface Morphometry

FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning

Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting

Federated Hypergradient Descent

Federated Coordinate Descent for Privacy-Preserving Multiparty Linear Regression

Federated Stochastic Primal-dual Learning with Differential Privacy

Federated Stochastic Gradient Descent Begets Self-Induced Momentum

Federated Optimization of Smooth Loss Functions