Search Results for author:
Found 6 papers, 1 papers with code
New Bounds on the Accuracy of Majority Voting for Multi-Class Classification
In this paper, we derive a new upper bound on the accuracy of the MVF for the multi-class classification problem.
TxAllo: Dynamic Transaction Allocation in Sharded Blockchain Systems
Blockchain sharding is a promising approach to this problem.
Semantic Code Search for Smart Contracts
To make the model more focused on the key contextual information, we use a multi-head attention network to generate embeddings for code features.
A Bytecode-based Approach for Smart Contract Classification
Our experimental studies on over 3, 300 real-world Ethereum smart contracts show that our model can classify smart contracts without source code and has better performance than baseline models.
How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning
This paper firstly considers the research problem of fairness in collaborative deep learning, while ensuring privacy.
Towards Fair and Privacy-Preserving Federated Deep Models
This problem can be addressed by either a centralized framework that deploys a central server to train a global model on the joint data from all parties, or a distributed framework that leverages a parameter server to aggregate local model updates.
