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Found 6 papers, 2 papers with code
Blockchain-based Federated Recommendation with Incentive Mechanism
The experimental results show that our proposed incentive mechanism can attract clients with superior training data to engage in the federal recommendation at a lower cost, which can increase the economic benefit of federal recommendation by 54. 9\% while improve the recommendation performance.
Speed-enhanced Subdomain Adaptation Regression for Long-term Stable Neural Decoding in Brain-computer Interfaces
We begin with preliminary experiments that reveal the temporal patterns of neural signal changes and identify three critical elements for effective recalibration: global alignment, conditional speed alignment, and feature-label consistency.
Clean-image Backdoor Attacks
To gather a significant quantity of annotated training data for high-performance image classification models, numerous companies opt to enlist third-party providers to label their unlabeled data.
CoMeta: Enhancing Meta Embeddings with Collaborative Information in Cold-start Problem of Recommendation
Specifically, for a new item: B-EG calculates the similarity-based weighted sum of the ID embeddings of old items as its base embedding; S-EG generates its shift embedding not only with its attribute features but also with the average ID embedding of the users who interacted with it.
Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios
Various attack methods against recommender systems have been proposed in the past years, and the security issues of recommender systems have drawn considerable attention.
FedRecAttack: Model Poisoning Attack to Federated Recommendation
Experimental results demonstrate that our proposed FedRecAttack achieves the state-of-the-art effectiveness while its side effects are negligible.
