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Federated Learning Based Distributed Localization of False Data Injection Attacks on Smart Grids
By employing federated learning for the detection of FDIA attacks, it is possible to train a model for the detection and localization of the attacks while preserving the privacy of sensitive user data.
Multi-Task and Transfer Learning for Federated Learning Applications
So, supporting federated learning with meta-learning tools such as multi-task learning and transfer learning will help enlarge the set of potential applications of federated learning by letting clients of different but related tasks share task-agnostic models that can be then further updated and tailored by each individual client for its particular task.
Clustered Scheduling and Communication Pipelining For Efficient Resource Management Of Wireless Federated Learning
In this way, instead of just waiting for the slower clients to finish their computation, more clients can participate in each iteration.
Robust and Fast Automatic Modulation Classification with CNN under Multipath Fading Channels
The proposed classifier is robust against realistic wireless channel impairments and in relation to that when the data sets that are utilized for testing and evaluating the proposed methods are considered, it is seen that RadioML2016. 10a is the main dataset utilized for testing and evaluation of the proposed methods.
