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Efficient Coded Multi-Party Computation at Edge Networks
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DOI：10.1109/TIFS.2023.3326970.png)
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Abstract
En 中文
Multi-party computation (MPC) is promising for designing privacy-preserving machine learning algorithms at edge networks. An emerging approach is coded-MPC (CMPC), which advocates the use of coded computation to improve the performance of MPC in terms of the required number of workers involved in computations. The current approach for designing CMPC algorithms is to merely combine efficient coded computation constructions with MPC. We show that this approach fails short of being efficient; e.g., entangled polynomial codes are not necessarily better than PolyDot codes in MPC setting, while they are always better for coded computation. Motivated by this observation, we propose a new construction; Adaptive Gap Entangled (AGE) polynomial codes for MPC. We show through analysis and simulations that MPC with AGE codes always perform better than existing CMPC algorithms in terms of the required number of workers as well as computation, storage, and communication overhead.
Keywords:
Codes
Servers
Privacy
Machine learning algorithms
Computational efficiency
Computational modeling
Data privacy
Adaptive gap entangled polynomial codes
multi-party computation
coded computation
edge computing
privacy
Journal
IF:
8
Papers:
5.2K
Citations:
2.3W
