Search Results - mohammedreza+ebrahimi

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FESA: Fast and Efficient Secure Aggregation for Privacy-Preserving Federated Learning
­Advantages: Protects user data during federated learning, ensuring individual privacy without compromising analysis. Minimizes data transfer, improves federated learning convergence speed and reducing resource consumption. Requires just one trustworthy assisting node, making implementation and scalability more straightforward. Summary: This...
Published: 9/3/2024   |   Inventor(s): Rouzbeh Behnia, Mohammedreza Ebrahimi, Thang Hoang, Balaji Padmanabhan
Keywords(s): Cybersecurity, Data Mining
Category(s): Technology Classifications > Data/AI > Cybersecurity - Data/AI, Technology Classifications > Computer Science > Computers Software & Information Technology
EW-Tune: A Framework for Privately Fine-Tuning Large Language Models with Differential Privacy
­Advantages: EW-Tune framework ensures robust privacy, preventing unauthorized access to sensitive AI data. Differing from conventional approaches, EW-Tune is custom-tailored for enhancing already established AI models, streamlining the process. Summary: This technology introduces EW-Tune, a pioneering solution aimed at refining AI language...
Published: 4/12/2024   |   Inventor(s): Mohammedreza Ebrahimi, Rouzbeh Behnia, Balaji Padmanabhan
Keywords(s): Artificial Intelligence, Cybersecurity
Category(s): Technology Classifications > Data/AI > Cybersecurity - Data/AI