Search Results for author:
Found 6 papers, 0 papers with code
PristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloud
Experimental results on simulation and the actual IBM quantum computer both prove the ability of PristiQ to provide high security for the quantum data while maintaining the model performance in QML.
Proof-of-Federated-Learning-Subchain: Free Partner Selection Subchain Based on Federated Learning
In the conducted experiments, the PoFLSC consensus supported the subchain manager to be aware of reservation priority and the core partition of contributors to establish and maintain a competitive subchain.
A Collaboration Strategy in the Mining Pool for Proof-of-Neural-Architecture Consensus
In many recent novel blockchain consensuses, the deep learning training procedure becomes the task for miners to prove their workload, thus the computation power of miners will not purely be spent on the hash puzzle.
The Stackelberg Equilibrium for One-sided Zero-sum Partially Observable Stochastic Games
We propose a space partition approach to solve the game iteratively and show that the value function of the leader is piece-wise linear and the value function of the follower is piece-wise constant for multiple stages.
Federated Dynamic GNN with Secure Aggregation
Given video data from multiple personal devices or street cameras, can we exploit the structural and dynamic information to learn dynamic representation of objects for applications such as distributed surveillance, without storing data at a central server that leads to a violation of user privacy?
DLBC: A Deep Learning-Based Consensus in Blockchains for Deep Learning Services
In this paper, we propose DLBC to exploit the computation power of miners for deep learning training as proof of useful work instead of calculating hash values.
