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 ...

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 ...

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

Atharv Kolhar, a staff test automation engineer at Figure AI, says the robotics industry needs a testing philosophy that ...

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Federated Learning (FL) is a collaborative machine learning technique where multiple clients work together with a central server to train a global model without sharing their private data. However, ...

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

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