QUANTIZATION ROBUST FEDERATED MACHINE LEARNING

QUANTIZATION ROBUST FEDERATED MACHINE LEARNING

Aspects described herein provide techniques for performing quantization robust federated learning of a machine learning model, comprising: receiving a model from a federated learning server; training the model using a local objective function, wherein the local objective function includes a modifica...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: FOURNARAKIS, Marios, NAGEL, Markus, REISSER, Matthias, LOUIZOS, Christos, GUPTA, Kartik
Format: Patent
Sprache:eng ; fre ; ger
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Beschreibung
Zusammenfassung:Aspects described herein provide techniques for performing quantization robust federated learning of a machine learning model, comprising: receiving a model from a federated learning server; training the model using a local objective function, wherein the local objective function includes a modification configured to increase quantization robustness at a client device; and transmitting to the federated learning server an updated model.