Search Results for author:
Found 7 papers, 4 papers with code
Scalable Federated Unlearning via Isolated and Coded Sharding
Federated unlearning has emerged as a promising paradigm to erase the client-level data effect without affecting the performance of collaborative learning models.
Blockchain-enabled Trustworthy Federated Unlearning
However, existing works require central servers to retain the historical model parameters from distributed clients, such that allows the central server to utilize these parameters for further training even, after the clients exit the training process.
FedSup: A Communication-Efficient Federated Learning Fatigue Driving Behaviors Supervision Framework
With the proliferation of edge smart devices and the Internet of Vehicles (IoV) technologies, intelligent fatigue detection has become one of the most-used methods in our daily driving.
When Deep Learning Meets Smart Contracts
Our tool can assist Solidity developers to efficiently identify repetitive smart contracts in the existing Ethereum blockchain, as well as checking their contract against a known set of bugs, which can help to improve the users' confidence in the reliability of the contract.
Software Engineering
Generating Question Titles for Stack Overflow from Mined Code Snippets
Stack Overflow has been heavily used by software developers as a popular way to seek programming-related information from peers via the internet.
Software Engineering
Checking Smart Contracts with Structural Code Embedding
However, many bugs and vulnerabilities have been identified in many contracts which raises serious concerns about smart contract security, not to mention that the blockchain systems on which the smart contracts are built can be buggy.
Software Engineering
SmartEmbed: A Tool for Clone and Bug Detection in Smart Contracts through Structural Code Embedding
In addition to the uses by individual developers, SmartEmbed can also be applied to studies of smart contracts in a large scale.
Software Engineering
