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Computational Code-Based Privacy in Coded Federated Learning
[article]
2022
arXiv
pre-print
We propose a privacy-preserving federated learning (FL) scheme that is resilient against straggling devices. An adaptive scenario is suggested where the slower devices share their data with the faster ones and do not participate in the learning process. The proposed scheme employs code-based cryptography to ensure computational privacy of the private data, i.e., no device with bounded computational power can obtain information about the other devices' data in feasible time. For a scenario with
arXiv:2202.13798v1
fatcat:674skja5a5ccvnkp6sxv4lsyji