Detecting Malicious Clients Based on Autoencoders in Federated Learning for Indoor Localization

Federated learning (FL) has recently emerged as a promising paradigm for WiFi fingerprinting–based indoor localization by enabling collaborative model training without sharing raw data. However, the ad hoc nature of FL makes it vulnerable to malicious clients that can launch data poisoning an...

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Hauptverfasser:	Akram Hussain, Muhammad Fahad, Shama Siddiqui, Muhammad Shoaib Siddiqui
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IEEE 2026-01-01
Schriftenreihe:	IEEE Access
Schlagworte:	Indoor localization WiFi fingerprinting federated learning malicious clients autoencoder
Online-Zugang:	https://ieeexplore.ieee.org/document/11450353/
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