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

November 11, 2024

Federated Learning Client Pruning for Noisy Labels
Mahdi Morafah, Hojin Chang, Chen Chen, Bill Lin
Federated Learning Noisy Label U CLIP Update

November 8, 2024

IPMN Risk Assessment under Federated Learning Paradigm
Hongyi Pan, Ziliang Hong, Gorkem Durak, Elif Keles, Halil Ertugrul Aktas, Yavuz Taktak, Alpay Medetalibeyoglu, Zheyuan Zhang, Yury Velichko, Concetto Spampinato, Ivo Schoots, Marco J. Bruno, Pallavi Tiwari, Candice Bolan, Tamas Gonda, Frank Miller, Rajesh N. Keswani, Michael B. Wallace, Ziyue Xu, Ulas Bagci
Federated Learning Pancreas Segmentation Intraductal Papillary Mucosal Neoplasm

November 7, 2024

Personalized Federated Learning for Cross-view Geo-localization
Christos Anagnostopoulos, Alexandros Gkillas, Nikos Piperigkos, Aris S. Lalos
Federated Learning Personalized Federated Learning Cross View Cross View Geo Localization

November 6, 2024

November 5, 2024

FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks
Duong H. Nguyen, Phi L. Nguyen, Truong T. Nguyen, Hieu H. Pham, Duc A. Tran
Federated Learning Backdoor Attack New Attack Blockchain Network Public Blockchain

November 2, 2024

November 1, 2024

Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication
Shaba Shaon, Tien Nguyen, Lina Mohjazi, Aryan Kaushik, Dinh C. Nguyen
Federated Learning Timely Communication Joint Optimization UAV Network Block Coordinate Descent Latency Optimization

October 30, 2024

FISC: Federated Domain Generalization via Interpolative Style Transfer and Contrastive Learning
Dung Thuy Nguyen, Taylor T. Johnson, Kevin Leach
Contrastive Learning Federated Learning Unseen Domain Arbitrary Style Transfer Heterogeneous Domain

October 29, 2024

BF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse
Wenbo Liu, Handi Chen, Edith C.H. Ngai
LeArning Abstract Federated Learning Federated Prompt Cooperation Ubiquitous Semantic Metaverse Secure Approach Meta Level Future METAVerse

October 26, 2024

FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference
Zihan Tan, Guancheng Wan, Wenke Huang, Mang Ye
Graph Neural Network Federated Learning Graph Structure Preference Learning Spectral Information Federated Graph Learning User Preference

October 24, 2024

October 23, 2024

Federated Learning

Papers

Federated Learning Client Pruning for Noisy Labels

IPMN Risk Assessment under Federated Learning Paradigm

Personalized Federated Learning for Cross-view Geo-localization

Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis

Cooperation and Personalization on a Seesaw: Choice-based FL for Safe Cooperation in Wireless Networks

FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks

Trustworthy Federated Learning: Privacy, Security, and Beyond

Analysis of regularized federated learning

Efficient and Robust Regularized Federated Recommendation

Optimizing Federated Learning by Entropy-Based Client Selection

Federated Learning with Relative Fairness

Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication

FISC: Federated Domain Generalization via Interpolative Style Transfer and Contrastive Learning

BF-Meta: Secure Blockchain-enhanced Privacy-preserving Federated Learning for Metaverse

FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference

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