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

August 19, 2024

Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasets
Xingrun Yan, Shiyuan Zuo, Rongfei Fan, Han Hu, Li Shen, Puning Zhao, Yong Luo
Data Set Federated Learning Convergence Analysis Non Convex Loss Function Edge Server Stable Convergence

August 18, 2024

August 16, 2024

RBLA: Rank-Based-LoRA-Aggregation for Fine-tuning Heterogeneous Models in FLaaS
Shuaijun Chen, Omid Tavallaie, Niousha Nazemi, Albert Y. Zomaya
Federated Learning Low Rank Adaptation Heterogeneous Model Rank Aggregation

August 13, 2024

August 11, 2024

Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data
Ji Liu, Juncheng Jia, Hong Zhang, Yuhui Yun, Leye Wang, Yang Zhou, Huaiyu Dai, Dejing Dou
Federated Learning Residual Momentum Data Sharing Adaptive Optimization Method Adaptive Pruning

August 7, 2024

A Blockchain-based Reliable Federated Meta-learning for Metaverse: A Dual Game Framework
Emna Baccour, Aiman Erbad, Amr Mohamed, Mounir Hamdi, Mohsen Guizani
Federated Learning Ubiquitous Semantic Metaverse Future METAVerse Coalition Game Metaverse Application Metaverse Service Metaverse User

August 6, 2024

FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG
Sai Puppala, Ismail Hossain, Md Jahangir Alam, Sajedul Talukder
Federated Learning Social Medium LLM Based Social Network Federated Prompt Cooperation Kg Rag Query Processing

August 5, 2024

Strategic Federated Learning: Application to Smart Meter Data Clustering
Hassan Mohamad, Chao Zhang, Samson Lasaulce, Vineeth S Varma, Mérouane Debbah, Mounir Ghogho
Application Proficiency Federated Learning Aggregated Data Energy Efficient Scheduling Harnessing Inherent Noise

August 3, 2024

Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learning
Xinlu Zhang, Yansha Deng, Toktam Mahmoodi
Federated Learning Resource Allocation Model Pruning Latency Constraint

July 31, 2024

FedBChain: A Blockchain-enabled Federated Learning Framework for Improving DeepConvLSTM with Comparative Strategy Insights
Gaoxuan Li, Chern Hong Lim, Qiyao Ma, Xinyu Tang, Hwa Hui Tew, Fan Ding, Xuewen Luo
Federated Learning FedAvg Algorithm ConvLSTM Network

July 30, 2024

Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharing
Eugenio Lomurno, Matteo Matteucci
Synthetic Data Federated Learning

July 29, 2024

FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models
Mingzhao Yang, Shangchao Su, Bin Li, Xiangyang Xue
Diffusion Model Federated Learning One Shot Federated Learning

July 27, 2024

Reducing Spurious Correlation for Federated Domain Generalization
Shuran Ma, Weiying Xie, Daixun Li, Haowei Li, Yunsong Li
Federated Learning Spurious Correlation Invariant Causal

July 25, 2024

July 23, 2024

Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory
Xiaojin Zhang, Wei Chen
Federated Learning Theoretical Analysis PRIvacy Leakage Privacy Attack Privacy Preserving Federated Learning Data Reconstruction Attack Linear Algebra

Federated Learning

Papers

Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasets

Seamless Integration: Sampling Strategies in Federated Learning Systems

Federated Graph Learning with Structure Proxy Alignment

RBLA: Rank-Based-LoRA-Aggregation for Fine-tuning Heterogeneous Models in FLaaS

Heterogeneity: An Open Challenge for Federated On-board Machine Learning

Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning

Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

A Blockchain-based Reliable Federated Meta-learning for Metaverse: A Dual Game Framework

FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG

Strategic Federated Learning: Application to Smart Meter Data Clustering

Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learning

FedBChain: A Blockchain-enabled Federated Learning Framework for Improving DeepConvLSTM with Comparative Strategy Insights

Federated Knowledge Recycling: Privacy-Preserving Synthetic Data Sharing

FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models

Reducing Spurious Correlation for Federated Domain Generalization

SCALE: Self-regulated Clustered federAted LEarning in a Homogeneous Environment

Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future

Lightweight Industrial Cohorted Federated Learning for Heterogeneous Assets

HF-Fed: Hierarchical based customized Federated Learning Framework for X-Ray Imaging

Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory