Computational Code-Based Privacy in Coded Federated Learning
This work addresses privacy and efficiency issues in federated learning for distributed systems, but it is incremental as it builds on existing FL methods with a new cryptographic approach.
The paper tackles the problem of straggling devices in federated learning by proposing a privacy-preserving scheme where slower devices share data with faster ones, using code-based cryptography to ensure computational privacy. For 25 devices, it achieves speed-ups of 4.7 and 4 for 92 and 128 bits security, respectively, while maintaining 95% accuracy on MNIST compared to conventional mini-batch FL.
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 \emph{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 25 devices, the proposed scheme achieves a speed-up of 4.7 and 4 for 92 and 128 bits security, respectively, for an accuracy of 95\% on the MNIST dataset compared with conventional mini-batch FL.