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11/23/2022
Federated Learning on Non-IID Graphs via Structural Knowledge Sharing
Graph neural networks (GNNs) have shown their superiority in modeling gr...
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09/21/2022
Federated Learning from Pre-Trained Models: A Contrastive Learning Approach
Federated Learning (FL) is a machine learning paradigm that allows decen...
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09/27/2021
How Low Can You Go? Practical cold-start performance limits in FaaS
Function-as-a-Service (FaaS) has recently emerged as a new cloud computi...
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08/24/2021
Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health
Privacy protection is an ethical issue with broad concern in Artificial ...
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08/24/2021
Federated Learning for Open Banking
Open banking enables individual customers to own their banking data, whi...
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05/01/2021
FedProto: Federated Prototype Learning over Heterogeneous Devices
The heterogeneity across devices usually hinders the optimization conver...
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06/05/2020
LSTM-based Anomaly Detection for Non-linear Dynamical System
Anomaly detection for non-linear dynamical system plays an important rol...
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02/07/2020
Dynamic Energy Dispatch in Isolated Microgrids Based on Deep Reinforcement Learning
This paper focuses on deep reinforcement learning (DRL)-based energy dis...
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07/22/2019
Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges
The Internet of Things (IoT) extends the Internet connectivity into bill...
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05/11/2019
