Σύγκριση μεθόδων

	Εύρωστη Ομοσπονδιακή Μάθηση	Online Federated Learning
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2017	2019–2021
Δημιουργός	Blanchard, P.; El Mhamdi, E. M.; Guerraoui, R.	McMahan, B. et al. (FL foundation); extended to online setting by multiple researchers c. 2019–2021
Τύπος	Distributed learning with Byzantine-tolerant aggregation	Distributed sequential learning
Θεμελιώδης πηγή	Blanchard, P., El Mhamdi, E. M., Guerraoui, R., & Stainer, J. (2017). Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent. Advances in Neural Information Processing Systems, 30.	Damaskinos, G., Guerraoui, R., Kermarrec, A.-M., Guirguis, A., Riviere, M., & Tempo, R. (2020). FLEET: Flexible and Efficient Federated Learning for Edge AI. Proceedings of Machine Learning and Systems (MLSys).
Εναλλακτικές ονομασίες	Byzantine-robust federated learning, fault-tolerant federated learning, robust FL, Byzantine-tolerant distributed learning	OFL, federated online learning, streaming federated learning, real-time federated learning
Συναφείς	6	5
Σύνοψη	Robust Federated Learning extends standard federated learning with Byzantine-tolerant aggregation rules that protect the global model against malicious, corrupted, or unreliable clients. Instead of naively averaging client gradients, robust aggregation methods such as coordinate-wise median or Krum filter out harmful updates so that a minority of adversarial participants cannot derail training.	Online Federated Learning (OFL) combines the privacy-preserving, decentralised structure of federated learning with the sequential, sample-by-sample update regime of online learning. Clients — such as mobile devices or edge sensors — receive a global model, update it on newly arriving local data without sharing raw observations, and contribute compressed updates to a central server that aggregates them in near-real-time.