Privacy-preserving in Blockchain-based Federated Learning systems

K.M., Sameera; Nicolazzo, Serena; Arazzi, Marco; Nocera, Antonino; K.A., Rafidha Rehiman; P., Vinod; Conti, Mauro

doi:10.1016/j.comcom.2024.04.024

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2024

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Cited by 3 publications

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Relation Extraction Techniques in Cyber Threat Intelligence

Arikkat,

Vinod,

K. A.

et al. 2024

Lecture Notes in Computer Science

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Relation Extraction Techniques in Cyber Threat Intelligence

Arikkat,

Vinod,

K. A.

et al. 2024

Lecture Notes in Computer Science

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Blockchain-Assisted Secure Energy Trading in Electricity Markets: A Tiny Deep Reinforcement Learning-Based Stackelberg Game Approach

Xiao,

Lin,

Lei

et al. 2024

Electronics

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Electricity markets are intricate systems that facilitate efficient energy exchange within interconnected grids. With the rise of low-carbon transportation driven by environmental policies and tech advancements, energy trading has become crucial. This trend towards Electric Vehicles (EVs) is bolstered by the pivotal role played by EV charging operators in providing essential charging infrastructure and services for widespread EV adoption. This paper introduces a blockchain-assisted secure electricity trading framework between EV charging operators and the electricity market with renewable energy sources. We propose a single-leader, multi-follower Stackelberg game between the electricity market and EV charging operators. In the two-stage Stackelberg game, the electricity market acts as the leader, deciding the price of electric energy. The EV charging aggregator leverages blockchain technology to record and verify energy trading transactions securely. The EV charging operators, acting as followers, then decide their demand for electric energy based on the set price. To find the Stackelberg equilibrium, we employ a Deep Reinforcement Learning (DRL) algorithm that tackles non-stationary challenges through policy, action space, and reward function formulation. To optimize efficiency, we propose the integration of pruning techniques into DRL, referred to as Tiny DRL. Numerical results demonstrate that our proposed schemes outperform traditional approaches.

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Consortium Blockchain Model for Decentralized of Healthcare Information

Ting,

Shin

2024

dcs

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Privacy-preserving in Blockchain-based Federated Learning systems

Cited by 3 publications

References 120 publications

Relation Extraction Techniques in Cyber Threat Intelligence

Relation Extraction Techniques in Cyber Threat Intelligence

Blockchain-Assisted Secure Energy Trading in Electricity Markets: A Tiny Deep Reinforcement Learning-Based Stackelberg Game Approach

Consortium Blockchain Model for Decentralized of Healthcare Information

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