Federated Learning

October 24, 2024

LanFL: Differentially Private Federated Learning with Large Language Models using Synthetic Samples
Huiyu Wu, Diego Klabjan
Large Language Model Full Model Synthetic Data Federated Learning Large Language Private Federated Learning Synthetic Sample
FedBaF: Federated Learning Aggregation Biased by a Foundation Model
Jong-Ik Park, Srinivasa Pranav, José M. F. Moura, Carlee Joe-Wong
Foundation Model Federated Learning

October 23, 2024

ProFL: Performative Robust Optimal Federated Learning
Xue Zheng, Tian Xie, Xuwei Tan, Aylin Yener, Xueru Zhang, Ali Payani, Myungjin Lee
Federated Learning Performative Prediction Robust Federated Learning Data Distribution Shift
Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data
Zhaomin Wu, Junyi Hou, Yiqun Diao, Bingsheng He
Federated Learning Raw Data Vertical Federated Learning Practical Application
Securing Federated Learning Against Novel and Classic Backdoor Threats During Foundation Model Integration
Xiaohuan Bi, Xi Li
Foundation Model Federated Learning Model Training Long Form Novel

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 Prognostic Model Degradation Representation 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

Papers

LanFL: Differentially Private Federated Learning with Large Language Models using Synthetic Samples

FedBaF: Federated Learning Aggregation Biased by a Foundation Model

ProFL: Performative Robust Optimal Federated Learning

Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data

Securing Federated Learning Against Novel and Classic Backdoor Threats During Foundation Model Integration

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