手法を比較

	ロバスト連合学習	ロバスト勾配ブースティング
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2017	2001
提唱者	Blanchard, P.; El Mhamdi, E. M.; Guerraoui, R.	Friedman, J. H. (with Huber loss from Huber, P. J.)
種類	Distributed learning with Byzantine-tolerant aggregation	Ensemble (boosted trees with robust loss)
原典	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.	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
別名	Byzantine-robust federated learning, fault-tolerant federated learning, robust FL, Byzantine-tolerant distributed learning	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees
関連	6	6
概要	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.	Robust Gradient Boosting is gradient boosting trained with outlier-resistant loss functions — most commonly the Huber loss or quantile (pinball) loss — instead of squared-error loss. Proposed in Friedman's seminal 2001 paper, this variant produces predictions far less distorted by extreme values or contaminated labels, while retaining the full predictive power of gradient-boosted trees.