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Research Areas

[1] Federated Learning, Distributed Learning

• Privacy-preserving federated learning using homomorphic encryption
• AsyncFL: Asynchronous federated learning using majority voting with quantized model updates
• FedCC: Federated learning with consensus confirmation for Byzantine attack resistance

[2] Privacy-preserving for Big Data and AI Applications

• Privacy-preserving reinforcement learning using homomorphic encryption in cloud computing infrastructures
• Privacy-preserving federated learning using homomorphic encryption

[3] AI for Cybersecurity

• Deep learning approach for detecting malicious activities over encrypted secure channels
• Deep learning approach for attack detection in controller area networks

[4] AI for Energy _{(building energy management, smart energy grid)}

• Building energy management for demand response using kernel lifelong learning
• Reinforcement learning based energy management algorithm for smart energy buildings
• Reinforcement learning based charging station management for electric vehicles

[5] Reinforcement Learning (RL) _{(Deep RL, Multi-agent RL)}

• Deep reinforcement learning-based routing on software-defined networks
• DIVERGENCE: Deep reinforcement learning-based adaptive traffic inspection and moving target defense countermeasure framework
• Deep reinforcement learning-based traffic sampling for multiple traffic analyzers on software-defined networks
• Multi-agent reinforcement learning-based resource management for end-to-end network slicing