A privacy-preserving marketing framework applies homomorphic encryption to perform machine learning on encrypted ...

Abstract: Recent years have witnessed a huge demand for artificial intelligence and machine learning applications in wireless edge networks to assist individuals with real-time services. Federated ...

Abstract: Federated learning enables participants to collaboratively train a global model through distributed training without sharing raw data. However, this distributed training is vulnerable to ...

The premise is straightforward — we are awash in biological data. The rapid growth of multiomics datasets (genomics, transcriptomics, proteomics, metabolomics, and radiomics) together with ...

Confidential computing (CC) emerges as an important solution, utilizing hardware-rooted Trusted Execution Environments to ...

The energy sector is becoming a highly connected cyber-physical ecosystem in which distributed energy resources, electric ...

Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?

Our paper about the robust FL algorithms evaluation with a new algorithm has been accepted by NeurIPS 2025, please check our FedGPS. In the open-source code of FedGPS we provide a clearer codebase for ...

VeryFL is a simple federated learning framework embedded with blockchain (Etherenum). Federated Learning side uses PyTorch while blockchain-side use Solidity deployed on Ethereum to implement on-chain ...

Spread the love“`html As we approach the midway point of the decade, it’s clear that the landscape of technology is evolving at an unprecedented pace. The tech trends for 2026 are not just interesting ...

Fraudulent Accounts Target Claude AI In a surprising turn of events, Anthropic has disclosed a significant security breach involving around 25,000 fraudulent accounts that were used to probe its ...

Meta ( META) had been using Google's Gemini models for tasks such as content moderation and scam detection because they ...