Federated Learning (FL) is an efficient and secure machine learning technique designed for decentralized computing systems such as fog edge computing. Its process employs frequent communications the participating local devices send updates, either gradients or parameters of their models, to a central server that aggregates them redistributes new weights devices. In FL, private data does not lea...

Deep learning has become the state of the art approach in many machine learning problems such as classi€cation. It has recently been shown that deep learning is highly vulnerable to adversarial perturbations. Taking the camera systems of self-driving cars as an example, small adversarial perturbations can cause the system to make errors in important tasks, such as classifying trac signs or det...

Owing to the distributed nature of denial-of-service attacks, it is tremendously challenging to detect such malicious behavior using traditional intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a game theoretic method is introduced, namely cooperative Game-based Fuzzy Q-learning (G-FQL). G-FQL adopts a combination of both the game theoretic approach and the ...

Making a decision, an agent must consider how his outcome can be influenced by possible actions of other agents. A ’best defense model’ for games involving uncertainty assumes usually that the opponents know everything about the actual situation and the player’s plans for certain. In this paper it’s argued that the assumption results in algorithms that are too cautious to be good in many game s...