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Found 7 papers, 1 papers with code
AiRacleX: Automated Detection of Price Oracle Manipulations via LLM-Driven Knowledge Mining and Prompt Generation
Decentralized finance (DeFi) applications depend on accurate price oracles to ensure secure transactions, yet these oracles are highly vulnerable to manipulation, enabling attackers to exploit smart contract vulnerabilities for unfair asset valuation and financial gain.
Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning
As we transition from Narrow Artificial Intelligence towards Artificial Super Intelligence, users are increasingly concerned about their privacy and the trustworthiness of machine learning (ML) technology.
Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning
In pFedSeq, the server maintains and trains a sequential learner, which processes a sequence of past adapter updates from clients and generates calibrations for personalized adapters.
Blockchain Data Analysis in the Era of Large-Language Models
Blockchain data analysis is essential for deriving insights, tracking transactions, identifying patterns, and ensuring the integrity and security of decentralized networks.
An Aggregation-Free Federated Learning for Tackling Data Heterogeneity
FedAF inherently avoids the issue of client drift, enhances the quality of condensed data amid notable data heterogeneity, and improves the global model performance.
FedUKD: Federated UNet Model with Knowledge Distillation for Land Use Classification from Satellite and Street Views
The need for a Federated approach in this application domain would be to avoid transfer of data from distributed locations and save network bandwidth to reduce communication cost.
NV-Tree: Reducing Consistency Cost for NVM-based Single Level Systems
Although the memory fence and CPU cacheline flush instructions can order memory writes to achieve data consistency, they introduce a significant overhead (more than 10X slower in performance).
