Heterogeneous Federated Learning

Heterogeneous federated learning (HFL) addresses the challenge of collaboratively training machine learning models across diverse devices with varying data distributions, computational capabilities, and model architectures, all while preserving data privacy. Current research focuses on developing robust aggregation techniques, including personalized model training and adaptive methods to handle skewed data, often employing techniques like knowledge distillation, prompt tuning, and prototype-based approaches. These advancements are crucial for enabling practical applications of federated learning in resource-constrained environments and scenarios with highly non-IID data, such as healthcare and IoT applications, where data heterogeneity is prevalent. The ultimate goal is to improve the accuracy, efficiency, and fairness of federated learning in real-world settings.

Papers

October 12, 2024

Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting
Chunlin Tian, Li Li, Kahou Tam, Yebo Wu, Chengzhong Xu
Full Model Heterogeneous Federated Learning Split and Fit

October 9, 2024

FedL2G: Learning to Guide Local Training in Heterogeneous Federated Learning
Jianqing Zhang, Yang Liu, Yang Hua, Jian Cao, Qiang Yang
Heterogeneous Federated Learning Heterogeneous Model Local Training Model Heterogeneity

October 3, 2024

FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning
Nurbek Tastan, Samuel Horvath, Martin Takac, Karthik Nandakumar
Data Heterogeneity Heterogeneous Federated Learning Unobserved Heterogeneity Warmup Period

September 27, 2024

September 20, 2024

SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework
Yuxin Zhang, Zheng Lin, Zhe Chen, Zihan Fang, Wenjun Zhu, Xianhao Chen, Jin Zhao, Yue Gao
Heterogeneous Federated Learning Low Earth Orbit Satellite Network Low Earth Orbit Satellite Hierarchically Fusing Satellite

August 15, 2024

Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization
Shunxin Guo, Hongsong Wang, Shuxia Lin, Zhiqiang Kou, Xin Geng
Personalized Federated Learning Heterogeneous Federated Learning Prototype Alignment Unknown Heterogeneity

July 23, 2024

On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness
Shengkun Zhu, Jinshan Zeng, Sheng Wang, Yuan Sun, Xiaodong Li, Yuan Yao, Zhiyong Peng
Native Robustness Procedural Fairness Heterogeneous Data Heterogeneous Federated Learning Alternating Direction Method of Multiplier Statistical Heterogeneity Superlinear Convergence Personalized Fl

July 3, 2024

Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generation
Yujin Shin, Kichang Lee, Sungmin Lee, You Rim Choi, Hyung-Sin Kim, JeongGil Ko
High Efficiency Heterogeneous Federated Learning Accuracy Improvement Attention Hypernetworks Hypernetwork Approach Weight Generation

June 15, 2024

Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data
Yukai Xu, Jingfeng Zhang, Yujie Gu
Heterogeneous Federated Learning Sensitive Patient Differential Privacy Mechanism Model Confidentiality

June 14, 2024

Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks
Yingchao Yu, Yuping Yan, Jisong Cai, Yaochu Jin
Convolutional Neural Network Deep Convolutional Neural Network Spiking Neural Network Heterogeneous Federated Learning Fusion Based Deep Learning

June 3, 2024

Accelerating Heterogeneous Federated Learning with Closed-form Classifiers
Eros Fanì, Raffaello Camoriano, Barbara Caputo, Marco Ciccone
Heterogeneous Federated Learning Ridge Regression Closed Set Federated Learning Model

May 30, 2024

Cross-Training with Multi-View Knowledge Fusion for Heterogenous Federated Learning
Zhuang Qi, Lei Meng, Weihao He, Ruohan Zhang, Yu Wang, Xin Qi, Xiangxu Meng
Heterogeneous Federated Learning Training Scheme Training Point Generalized Knowledge

May 27, 2024

FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation
Yuting Ma, Lechao Cheng, Yaxiong Wang, Zhun Zhong, Xiaohua Xu, Meng Wang
Prompt Tuning Heterogeneous Federated Learning Logit Distillation Heterogeneity Challenge

May 16, 2024

Advances in Robust Federated Learning: Heterogeneity Considerations
Chuan Chen, Tianchi Liao, Xiaojun Deng, Zihou Wu, Sheng Huang, Zibin Zheng
Heterogeneous Federated Learning Unobserved Heterogeneity

May 11, 2024

Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations
Yumeng Shao, Jun Li, Long Shi, Kang Wei, Ming Ding, Qianmu Li, Zengxiang Li, Wen Chen, Shi Jin
General Analysis Federated Learning Heterogeneous Federated Learning Efficient Optimization Model Aggregation Asynchronous Aggregation

April 27, 2024

pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learning
Liping Yi, Han Yu, Chao Ren, Heng Zhang, Gang Wang, Xiaoguang Liu, Xiaoxiao Li
Heterogeneous Federated Learning Scalable Personalization Federated Learning Approach Personalized Prediction Adaptive Mixture Local Heterogeneous Model

April 20, 2024

Breaking the Memory Wall for Heterogeneous Federated Learning with Progressive Training
Yebo Wu, Li Li, Chunlin Tian, Chengzhong Xu
Heterogeneous Federated Learning Progressive Alignment Progressive Training Personalized Fl Peak Memory

April 14, 2024

Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentation
Jieyi Tan, Yansheng Li, Sergey A. Bartalev, Bo Dang, Wei Chen, Yongjun Zhang, Liangqi Yuan
Remote Sensing Image Heterogeneous Federated Learning Remote Sensing Image Semantic Segmentation Data Challenge Spatial Heterogeneity

March 24, 2024

Heterogeneous Federated Learning with Splited Language Model
Yifan Shi, Yuhui Zhang, Ziyue Huang, Xiaofeng Yang, Li Shen, Wei Chen, Xueqian Wang
Language Model Vision Transformer Split Learning Heterogeneous Federated Learning Gradient Inversion Attack Federated Split Learning

Heterogeneous Federated Learning

Papers

Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting

FedL2G: Learning to Guide Local Training in Heterogeneous Federated Learning

FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning

HSTFL: A Heterogeneous Federated Learning Framework for Misaligned Spatiotemporal Forecasting

Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration

SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework

Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization

On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness

Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generation

Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data

Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks

Accelerating Heterogeneous Federated Learning with Closed-form Classifiers

Cross-Training with Multi-View Knowledge Fusion for Heterogenous Federated Learning

FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation

Advances in Robust Federated Learning: Heterogeneity Considerations

Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations

pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learning

Breaking the Memory Wall for Heterogeneous Federated Learning with Progressive Training

Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentation

Heterogeneous Federated Learning with Splited Language Model