Sammenlign metoder

	Robust Federated Learning	Føderert læring
Fagfelt	Maskinlæring	Personvern
Familie	Machine learning	Machine learning
Opprinnelsesår	2017	2017
Opphavsperson	Blanchard, P.; El Mhamdi, E. M.; Guerraoui, R.	McMahan et al.
Type	Distributed learning with Byzantine-tolerant aggregation	Distributed privacy-preserving machine learning
Opprinnelig kilde	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.	McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, 1273–1282.
Alias	Byzantine-robust federated learning, fault-tolerant federated learning, robust FL, Byzantine-tolerant distributed learning	Collaborative Learning, Decentralized Learning, FedAvg, Federe Öğrenme
Relaterte	6	3
Sammendrag	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.	Federated Learning is a distributed machine learning paradigm introduced by McMahan et al. in 2017 in which a global model is trained collaboratively across multiple decentralized clients — such as mobile devices or hospital systems — without ever transferring raw data to a central server. Each participant computes model updates locally using its private data; only those updates, not the underlying data, are communicated and aggregated by the server to improve the shared model.