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Incentive mechanisms for federated learning: From economic and game theoretic perspective
Federated learning (FL) becomes popular and has shown great potentials in training large-
scale machine learning (ML) models without exposing the owners' raw data. In FL, the data …
scale machine learning (ML) models without exposing the owners' raw data. In FL, the data …
[HTML][HTML] Balancing privacy and performance in federated learning: A systematic literature review on methods and metrics
Federated learning (FL) as a novel paradigm in Artificial Intelligence (AI), ensures enhanced
privacy by eliminating data centralization and brings learning directly to the edge of the …
privacy by eliminating data centralization and brings learning directly to the edge of the …
Decentralized P2P federated learning for privacy-preserving and resilient mobile robotic systems
Swarms of mobile robots are being widely applied for complex tasks in various practical
scenarios toward modern smart industry. Federated learning (FL) has been developed as a …
scenarios toward modern smart industry. Federated learning (FL) has been developed as a …
Tackling system and statistical heterogeneity for federated learning with adaptive client sampling
Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial
participation) when the number of participants is large and the server's communication …
participation) when the number of participants is large and the server's communication …
Fedproc: Prototypical contrastive federated learning on non-iid data
Federated learning (FL) enables multiple clients to jointly train high-performance deep
learning models while maintaining the training data locally. However, it is challenging to …
learning models while maintaining the training data locally. However, it is challenging to …
High-quality model aggregation for blockchain-based federated learning via reputation-motivated task participation
Federated learning is an emerging paradigm to conduct the machine learning
collaboratively but avoid the leakage of original data. Then, how to motivate the data owners …
collaboratively but avoid the leakage of original data. Then, how to motivate the data owners …
A profit-maximizing model marketplace with differentially private federated learning
Existing machine learning (ML) model marketplaces generally require data owners to share
their raw data, leading to serious privacy concerns. Federated learning (FL) can partially …
their raw data, leading to serious privacy concerns. Federated learning (FL) can partially …
EEFED: Personalized federated learning of execution&evaluation dual network for CPS intrusion detection
In the modern interconnected world, intelligent networks and computing technologies are
increasingly being incorporated in industrial systems. However, this adoption of advanced …
increasingly being incorporated in industrial systems. However, this adoption of advanced …
Towards online privacy-preserving computation offloading in mobile edge computing
Mobile Edge Computing (MEC) is a new paradigm where mobile users can offload
computation tasks to the nearby MEC server to reduce their resource consumption. Some …
computation tasks to the nearby MEC server to reduce their resource consumption. Some …
Faithful edge federated learning: Scalability and privacy
Federated learning enables machine learning algorithms to be trained over decentralized
edge devices without requiring the exchange of local datasets. Successfully deploying …
edge devices without requiring the exchange of local datasets. Successfully deploying …