Model Aggregation

Model aggregation in federated learning aims to combine locally trained models from multiple decentralized sources into a single, improved global model without directly sharing sensitive data. Current research focuses on addressing challenges like data heterogeneity (non-IID data) and resource constraints through techniques such as weighted averaging, knowledge distillation, and meta-learning, often employing various similarity metrics to identify compatible models for aggregation. These advancements are crucial for enabling collaborative machine learning in privacy-sensitive applications across diverse settings, including healthcare, IoT, and vehicular networks, improving both model accuracy and robustness.

Papers

December 4, 2023

FLea: Improving federated learning on scarce and label-skewed data via privacy-preserving feature augmentation
Tong Xia, Abhirup Ghosh, Cecilia Mascolo
LeArning Abstract Label Distribution Model Aggregation Parallel Training Local Learning Rat Image

November 29, 2023

Aggregation Model Hyperparameters Matter in Digital Pathology
Gustav Bredell, Marcel Fischer, Przemyslaw Szostak, Samaneh Abbasi-Sureshjani, Alvaro Gomariz
Digital Pathology Model Aggregation Feature Extractor Model

October 11, 2023

RaftFed: A Lightweight Federated Learning Framework for Vehicular Crowd Intelligence
Changan Yang, Yaxing Chen, Yao Zhang, Helei Cui, Zhiwen Yu, Bin Guo, Zheng Yan, Zijiang Yang
Model Aggregation Vehicular Ad Hoc Network Network Intelligence Crowd Intelligence

July 27, 2023

Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification
Marawan Elbatel, Hualiang Wang, Robert Martí, Huazhu Fu, Xiaomeng Li
Model Aggregation Auxiliary Task Client Model Self Supervised Pre Trained Model Imbalanced Medical Image Self Supervised Auxiliary Task

July 21, 2023

Mitigating Communications Threats in Decentralized Federated Learning through Moving Target Defense
Enrique Tomás Martínez Beltrán, Pedro Miguel Sánchez Sánchez, Sergio López Bernal, Gérôme Bovet, Manuel Gil Pérez, Gregorio Martínez Pérez, Alberto Huertas Celdrán
Decentralized Federated Learning Model Aggregation Threat Model Attack Method Security Challenge Moving Target Defense Network Security

June 21, 2023

Timely Asynchronous Hierarchical Federated Learning: Age of Convergence
Purbesh Mitra, Sennur Ulukus
Federated Learning Early Stage Convergence Speech Based Age Model Aggregation Asynchronous Federated Learning Edge Cloud Dense Environment Training Iteration

June 20, 2023

Towards Theory-based Moral AI: Moral AI with Aggregating Models Based on Normative Ethical Theory
Masashi Takeshita, Rzepka Rafal, Kenji Araki
Model Aggregation Moral Judgment Moral Dilemma Ethical Principle Cultural Commonsense

June 16, 2023

Fedstellar: A Platform for Decentralized Federated Learning
Enrique Tomás Martínez Beltrán, Ángel Luis Perales Gómez, Chao Feng, Pedro Miguel Sánchez Sánchez, Sergio López Bernal, Gérôme Bovet, Manuel Gil Pérez, Gregorio Martínez Pérez, Alberto Huertas Celdrán
Diverse Platform Decentralized Federated Learning Model Aggregation Heterogeneous Network Centralized Federated Learning

June 2, 2023

Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs
Tengfei Ma, Trong Nghia Hoang, Jie Chen
Full Model Decentralized Data Model Aggregation Local Learning Consensus Graph Unique Feature

May 25, 2023

pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning
Jiahao Tan, Yipeng Zhou, Gang Liu, Jessie Hui Wang, Shui Yu
Data Heterogeneity Personalized Federated Learning Model Aggregation Aspect Term Extraction Client Heterogeneity

May 17, 2023

Simplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation
Jiong Zhu, Aishwarya Reganti, Edward Huang, Charles Dickens, Nikhil Rao, Karthik Subbian, Danai Koutra
Neural Network Training Data Model Aggregation Centralized Training Training Graph GNN Training

May 7, 2023

MrTF: Model Refinery for Transductive Federated Learning
Xin-Chun Li, Yang Yang, De-Chuan Zhan
Gradient Based Model Aggregation Transductive Linear Label Refinery

April 28, 2023

Hierarchical and Decentralised Federated Learning
Omer Rana, Theodoros Spyridopoulos, Nathaniel Hudson, Matt Baughman, Kyle Chard, Ian Foster, Aftab Khan
Machine Learning Model Decentralized Federated Learning Model Aggregation Hierarchical Federated Learning Global Deep

April 25, 2023

Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Things
Aditya Pribadi Kalapaaking, Ibrahim Khalil, Mohammad Saidur Rahman, Mohammed Atiquzzaman, Xun Yi, Mahathir Almashor
Federated Learning Secure Aggregation Model Aggregation Blockchain Network Trusted Execution Environment

February 24, 2023

FedPDC:Federated Learning for Public Dataset Correction
Yuquan Zhang, Yongquan Zhang
Model Aggregation Machine Learning Paradigm FedProx Algorithm Correction Dataset

December 5, 2022

HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation
Jun Xia, Yi Zhang, Zhihao Yue, Ming Hu, Xian Wei, Mingsong Chen
Self Distillation Part Whole Hierarchy Heterogeneous Federated Learning Model Aggregation Heterogeneous Model Heterogeneous Artificial Intelligence

November 15, 2022

Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges
Enrique Tomás Martínez Beltrán, Mario Quiles Pérez, Pedro Miguel Sánchez Sánchez, Sergio López Bernal, Gérôme Bovet, Manuel Gil Pérez, Gregorio Martínez Pérez, Alberto Huertas Celdrán
New Framework Technical Challenge Internal State Decentralized Federated Learning Model Aggregation Federated Setting Basic Concept Centralized Model

October 19, 2022

Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learning
Liangkun Yu, Xiang Sun, Rana Albelaihi, Chen Yi
Federated Learning Federated Prompt Cooperation Model Aggregation Collaborative Machine Learning Faster Convergence Speed

October 15, 2022

FedCross: Towards Accurate Federated Learning via Multi-Model Cross Aggregation
Ming Hu, Peiheng Zhou, Zhihao Yue, Zhiwei Ling, Yihao Huang, Yang Liu, Mingsong Chen
Federated Learning Model Aggregation Fusion Weight Injection Based Training

October 4, 2022

Domain Discrepancy Aware Distillation for Model Aggregation in Federated Learning
Shangchao Su, Bin Li, Xiangyang Xue
Knowledge Distillation Model Aggregation Domain Discrepancy Knowledge Aggregation

Model Aggregation

Papers

FLea: Improving federated learning on scarce and label-skewed data via privacy-preserving feature augmentation

Aggregation Model Hyperparameters Matter in Digital Pathology

RaftFed: A Lightweight Federated Learning Framework for Vehicular Crowd Intelligence

Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification

Mitigating Communications Threats in Decentralized Federated Learning through Moving Target Defense

Timely Asynchronous Hierarchical Federated Learning: Age of Convergence

Towards Theory-based Moral AI: Moral AI with Aggregating Models Based on Normative Ethical Theory

Fedstellar: A Platform for Decentralized Federated Learning

Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs

pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning

Simplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation

MrTF: Model Refinery for Transductive Federated Learning

Hierarchical and Decentralised Federated Learning

Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Things

FedPDC:Federated Learning for Public Dataset Correction

HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation

Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges

Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learning

FedCross: Towards Accurate Federated Learning via Multi-Model Cross Aggregation

Domain Discrepancy Aware Distillation for Model Aggregation in Federated Learning