A structured review of Byzantine-robust distributed and decentralized inference and learning, with tables of guarantees and experimental comparisons of screening-based aggregation methods.
A little is enough: Circumventing defenses for distributed learning,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Adversary-resilient Distributed and Decentralized Statistical Inference and Machine Learning: An Overview of Recent Advances Under the Byzantine Threat Model
A structured review of Byzantine-robust distributed and decentralized inference and learning, with tables of guarantees and experimental comparisons of screening-based aggregation methods.