Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System. (arXiv:2205.04855v1 [cs.MA])

Decentralized learning is an efficient emerging paradigm for boosting the
computing capability of multiple bounded computing agents. In the big data era,
performing inference within the distributed and federated learning (DL and FL)
frameworks, the central server needs to process a large amount of data while
relying on various agents to perform multiple distributed training tasks.
Considering the decentralized computing topology, privacy has become a
first-class concern. Moreover, assuming limited information processing
capability for the agents calls for a sophisticated …

agent distributed prediction privacy system trade