Federated Learning

Federated learning (FL) is a decentralized machine learning approach enabling collaborative model training across multiple devices without directly sharing their data, thereby preserving privacy. Current research focuses on addressing challenges like data heterogeneity (non-IID data), communication efficiency (e.g., using scalar updates or spiking neural networks), and robustness to adversarial attacks or concept drift, often employing techniques such as knowledge distillation, James-Stein estimators, and adaptive client selection. FL's significance lies in its potential to unlock the power of massive, distributed datasets for training sophisticated models while adhering to privacy regulations and ethical considerations, with applications spanning healthcare, IoT, and other sensitive domains.

Papers

October 22, 2024

Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks
Tao Li, Henger Li, Yunian Pan, Tianyi Xu, Zizhan Zheng, Quanyan Zhu
Federated Learning Adaptive Importance Poisoning Attack Meta Stackelberg

October 19, 2024

Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfer
Keting Yin, Jiayi Mao
Strong Generalization Federated Learning Knowledge Transfer Personalized Federated Learning Feature Aggregation Privacy Preserving Machine Learning Personalized Model

October 18, 2024

FedSpaLLM: Federated Pruning of Large Language Models
Guangji Bai, Yijiang Li, Zilinghan Li, Liang Zhao, Kibaek Kim
Federated Learning

October 17, 2024

Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information
Ömer Faruk Akgül, Rajgopal Kannan, Viktor Prasanna
Federated Learning Conformal Prediction Variational Method Subgraph Structure Thy Neighbor Graph Datasets Federated Graph Neural Network

October 15, 2024

Why Go Full? Elevating Federated Learning Through Partial Network Updates
Haolin Wang, Xuefeng Liu, Jianwei Niu, Wenkai Guo, Shaojie Tang
Federated Learning Multi Layer Model Convergence Information System Update

October 14, 2024

A Two-Stage Federated Learning Approach for Industrial Prognostics Using Large-Scale High-Dimensional Signals
Yuqi Su, Xiaolei Fang
Federated Learning Prognostic Prediction Degradation Representation Prognostic Model Dimensional Signal Industrial Prognostic

October 13, 2024

Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid
Vineet Jagadeesan Nair, Lucas Pereira
Federated Learning Early Stage Convergence Power Grid Load Forecasting Faster Convergence Energy Storage Solar Generation

October 12, 2024

A New Perspective to Boost Performance Fairness for Medical Federated Learning
Yunlu Yan, Lei Zhu, Yuexiang Li, Xinxing Xu, Rick Siow Mong Goh, Yong Liu, Salman Khan, Chun-Mei Feng
LeArning Abstract Federated Learning Domain Shift New Perspective Model Fairness Proportional Fairness Feature Shift

October 11, 2024

October 10, 2024

Scalable and Resource-Efficient Second-Order Federated Learning via Over-the-Air Aggregation
Abdulmomen Ghalkha, Chaouki Ben Issaid, Mehdi Bennis
Federated Learning Hessian Matrix Faster Convergence Air Aggregation Network Bottleneck

October 9, 2024

Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare
Xenia Konti, Hans Riess, Manos Giannopoulos, Yi Shen, Michael J. Pencina, Nicoleta J. Economou-Zavlanos, Michael M. Zavlanos
Case Study Federated Learning Healthcare System Heterogeneous Data Robust Clustering Clustered Federated Learning Client Data Heterogeneity

October 5, 2024

ConDa: Fast Federated Unlearning with Contribution Dampening
Vikram S Chundawat, Pushkar Niroula, Prasanna Dhungana, Stefan Schoepf, Murari Mandal, Alexandra Brintrup
Federated Learning Membership Inference Attack Model Training Unlearning Framework Federated Unlearning

October 4, 2024

October 3, 2024

FedScalar: A Communication efficient Federated Learning
M. Rostami, S. S. Kia
Machine Learning Federated Learning Timely Communication Communication Efficiency Non Convex Loss Function

October 2, 2024

September 30, 2024

Federated Learning

Papers

Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks

Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfer

FedSpaLLM: Federated Pruning of Large Language Models

Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information

Why Go Full? Elevating Federated Learning Through Partial Network Updates

A Two-Stage Federated Learning Approach for Industrial Prognostics Using Large-Scale High-Dimensional Signals

Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid

A New Perspective to Boost Performance Fairness for Medical Federated Learning

DistDD: Distributed Data Distillation Aggregation through Gradient Matching

SoK: Verifiable Cross-Silo FL

Scalable and Resource-Efficient Second-Order Federated Learning via Over-the-Air Aggregation

Distributionally Robust Clustered Federated Learning: A Case Study in Healthcare

ConDa: Fast Federated Unlearning with Contribution Dampening

FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein Estimator

Influence-oriented Personalized Federated Learning

FedScalar: A Communication efficient Federated Learning

FLAME: Adaptive and Reactive Concept Drift Mitigation for Federated Learning Deployments

A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers

Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"

Leveraging Pre-trained Models for Robust Federated Learning for Kidney Stone Type Recognition