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

November 12, 2024

Dual-Criterion Model Aggregation in Federated Learning: Balancing Data Quantity and Quality
Haizhou Zhang, Xianjia Yu, Tomi Westerlund
Quality Issue Data Aggregation Model Aggregation Quantity Aware Aggregation Method Data Balancing Client Data

October 27, 2024

Detection of adrenal anomalous findings in spinal CT images using multi model graph aggregatio
Shabalin Carmel, Shenkman Israel, Shelef Ilan, Ben-Arie Gal, Alex Geftler, Shahar Yuval
Data Detection Computed Tomography Imaging Technique Model Aggregation MRI Scan Spinal X Ray Lumbar SPINE

September 25, 2024

Model aggregation: minimizing empirical variance outperforms minimizing empirical error
Théo Bourdais, Houman Owhadi
Empirical Risk Minimization Model Aggregation Empirical Risk Systematic Error Optimal Aggregation Error Minimizing Noise

September 16, 2024

On the effects of similarity metrics in decentralized deep learning under distributional shift
Edvin Listo Zec, Tom Hagander, Eric Ihre-Thomason, Sarunas Girdzijauskas
Deep Learning Mixed Effect Distribution Shift Privacy Preserving Decentralized Learning Model Aggregation Similarity Metric

August 1, 2024

Aggregation Models with Optimal Weights for Distributed Gaussian Processes
Haoyuan Chen, Rui Tuo
Gaussian Process Model Aggregation Consistent Aggregation Sparse Variational Optimal Weight Prediction Stability

July 17, 2024

A Framework for testing Federated Learning algorithms using an edge-like environment
Felipe Machado Schwanck, Marcos Tomazzoli Leipnitz, Joel Luís Carbonera, Juliano Araujo Wickboldt
New Framework Edge Computing Model Aggregation Kubernetes Cluster Centralized Model Edge Environment

June 23, 2024

Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning
Zahir Alsulaimawi
Meta Learning Model Aggregation Aware Aggregator Federated Meta Learning Modern News Aggregator

June 14, 2024

Federated Learning with Flexible Architectures
Jong-Ik Park, Carlee Joe-Wong
Model Aggregation Aggregation Method Federated Learning Training

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 19, 2024

KoReA-SFL: Knowledge Replay-based Split Federated Learning Against Catastrophic Forgetting
Zeke Xia, Ming Hu, Dengke Yan, Ruixuan Liu, Anran Li, Xiaofei Xie, Mingsong Chen
Catastrophic Forgetting Knowledge Based Model Aggregation Split Federated Learning Knowledge Replay

April 14, 2024

MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes
Xin-Chun Li, Shaoming Song, Yinchuan Li, Bingshuai Li, Yunfeng Shao, Yang Yang, De-Chuan Zhan
Data Heterogeneity Model Aggregation Private Model Model Personalization mAP Decentralized Framework

March 22, 2024

Conformal online model aggregation
Matteo Gasparin, Aaditya Ramdas
Machine Learning Model Conformal Prediction Model Selection Prediction Set Model Aggregation Conformal FRP@TPR95 Metric

March 3, 2024

February 16, 2024

FedKit: Enabling Cross-Platform Federated Learning for Android and iOS
Sichang He, Beilong Tang, Boyan Zhang, Jiaoqi Shao, Xiaomin Ouyang, Daniel Nata Nugraha, Bing Luo
Mobile Application Model Aggregation Cross Device Federated Learning Hardware Aware Training

February 6, 2024

Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression
Xiaoxin Su, Yipeng Zhou, Laizhong Cui, Song Guo
Federated Learning Model Compression Global Model Model Aggregation Consensus Learning

January 24, 2024

Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems
Jikun Gao, Ioannis Mavromatis, Peizheng Li, Pietro Carnelli, Aftab Khan
Global Model Model Aggregation Asynchronous Federated Learning

January 17, 2024

Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients
Mohamed Ads, Hesham ElSawy, Hossam S. Hassanein
Federated Learning Model Aggregation Heterogeneous Client

December 28, 2023

FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning
Ho Man Kwan, Shenghui Song
Knowledge Distillation Model Aggregation Federated Distillation

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

Model Aggregation

Papers

Dual-Criterion Model Aggregation in Federated Learning: Balancing Data Quantity and Quality

Detection of adrenal anomalous findings in spinal CT images using multi model graph aggregatio

Model aggregation: minimizing empirical variance outperforms minimizing empirical error

On the effects of similarity metrics in decentralized deep learning under distributional shift

Aggregation Models with Optimal Weights for Distributed Gaussian Processes

A Framework for testing Federated Learning algorithms using an edge-like environment

Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning

Federated Learning with Flexible Architectures

Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations

KoReA-SFL: Knowledge Replay-based Split Federated Learning Against Catastrophic Forgetting

MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes

Conformal online model aggregation

Quantized Hierarchical Federated Learning: A Robust Approach to Statistical Heterogeneity

Asyn2F: An Asynchronous Federated Learning Framework with Bidirectional Model Aggregation

FedKit: Enabling Cross-Platform Federated Learning for Android and iOS

Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression

Mitigating System Bias in Resource Constrained Asynchronous Federated Learning Systems

Risk-Aware Accelerated Wireless Federated Learning with Heterogeneous Clients

FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning

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