Federated Learning: Applications and Future Directions

Dear Colleagues,

Federated learning (FL) addresses several relevant challenges in this space, and it has thus become an important research area in machine learning and AI. Federated learning can be used when one wants to train a machine learning model based on a dataset stored across multiple locations without the ability to move the data to any central location.

One class of applications relates to when data are generated by different smartphone app users, staying on users’ phones for privacy reasons. Another class of applications involves data collected by various organizations, which are unable to be shared due to confidentiality reasons. Nevertheless, the same restrictions can also be present independent of privacy concerns, such as in the case of data streams collected by IoT devices or self-driving cars, which need to be processed on the device because it is infeasible to transmit and store the sheer amount of data.

This Special Issue aims to collect several novel contributions and research experiences regarding federated learning studies and applications from different research communities concerning different but complementary solutions and proposals to mitigate issues and optimize Federated Learning algorithms.

The topics of the Special Issue include (but are not limited to):

• Advances, novel issues, and open challenges in federated learning;
• Federated learning trust policies and strategies;
• Security concerns with federated learning;
• Performance evaluation methods, metrics and tools of federated learning systems;
• Performance optimization of federated learning models;
• Privacy concerns and federated learning;
• Case Studies and applications of federated learning;
• Federated learning frameworks and tools employment and comparisons;
• Federated learning and Blockchain;
• Federated learning for IoT;
• Federated learning for smart grids;
• Federated learning for energy efficiency In IoT;
• Federated learning for industrial applications;
• Federated learning and graph-based approaches for fraud detection;
• Federated learning for intrusion detection In IoT;
• Federated learning with edge computing for cybersecurity In IoT;
• Federated learning for privacy preservation of users in social media apps.

Dr. Giovanni Paragliola
Dr. Laura Verde
Dr. Fiammetta Marulli
Dr. Rosario Catelli
Guest Editors

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