Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation

2023/01/30 by Muhammad Irfan Khan, Khan, Muhammad Irfan, Mohammad Ayyaz Azeem +9 · 3 citations
Computer Science · Medicine · Neuroscience · #Artificial Intelligence (cs.AI) #Brain Tumor Detection and Classification #Distributed #FOS: Computer and information sciences #Glioma Diagnosis and Treatment #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Parallel #Privacy-Preserving Technologies in Data #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2301.12617

openalex publication_date 2023/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In federated learning (FL), the global model at the server requires an efficient mechanism for weight aggregation and a systematic strategy for collaboration selection to manage and optimize communication payload. We introduce a practical and cost-efficient method for regularized weight aggregation and propose a laborsaving technique to select collaborators per round. We illustrate the performance of our method, regularized similarity weight aggregation (RegSimAgg), on the Federated Tumor Segmentation (FeTS) 2022 challenge's federated training (weight aggregation) problem. Our scalable approach is principled, frugal, and suitable for heterogeneous non-IID collaborators. Using FeTS2021 evaluation criterion, our proposed algorithm RegSimAgg stands at 3rd position in the final rankings of FeTS2022 challenge in the weight aggregation task. Our solution is open sourced at: \urlhttps://github.com/dskhanirfan/FeTS2022

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