Byzantine-Resilient Secure Federated Learning
نویسندگان
چکیده
Secure federated learning is a privacy-preserving framework to improve machine models by training over large volumes of data collected mobile users. This achieved through an iterative process where, at each iteration, users update global model using their local datasets. Each user then masks its via random keys, and the masked are aggregated central server compute for next iteration. As updates protected masks, cannot observe true values. presents major challenge resilience against adversarial (Byzantine) users, who can manipulate modifying or Towards addressing this challenge, paper first single-server Byzantine-resilient secure aggregation (BREA) learning. BREA based on integrated stochastic quantization, verifiable outlier detection, approach guarantee Byzantine-resilience, privacy, convergence simultaneously. We provide theoretical privacy guarantees characterize fundamental trade-offs in terms network size, dropouts, protection. Our experiments demonstrate presence Byzantine comparable accuracy conventional benchmarks.
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ژورنال
عنوان ژورنال: IEEE Journal on Selected Areas in Communications
سال: 2021
ISSN: ['0733-8716', '1558-0008']
DOI: https://doi.org/10.1109/jsac.2020.3041404