Robust Aggregation for Federated Learning

We present a novel approach to federated learning that endows its aggregation process with greater robustness to potential poisoning of local data or model parameters of participating devices. The proposed approach, Robust Federated Aggregation (RFA), relies on the aggregation of updates using the g...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE transactions on signal processing Ročník 70; s. 1142 - 1154
Hlavní autori:	Pillutla, Krishna, Kakade, Sham M., Harchaoui, Zaid
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Agglomeration Aggregates Algorithms Collaborative work Computer vision corrupted updates Corruption data privacy distributed learning Federated learning Machine learning Natural language processing Optimization Privacy robust aggregation Robustness Servers Signal processing algorithms
ISSN:	1053-587X, 1941-0476
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!