Federated Learning Systems: Mathematical Modeling and Internet of Things

Since the creation of the first computer in 1948, notably thanks to the work of Alan Turing with the universal machine in 1936, computing has become an integral part of our lives and occupies an important place in our society. Today, it would be hard to imagine a world without computers and mobile phones. All these tools have made it possible to develop new activities and also to increase our knowledge. Indeed, the calculation units are more and more powerful, and today it is possible to solve complex problems. It is in this logic of learning that machine learning was born in the late 1950s, with the creation of a program by Arthur Samuel, an American computer scientist who pioneered the development of artificial intelligence. This program, created for the giant IBM, played checkers by improving with each turn. Machine learning is a branch of artificial intelligence that aims to develop algorithms and models that can learn from data and make decisions or make predictions without being programmed. It is a booming technology that could revolutionize many areas of today’s society. The continuous development of machine learning is crucial to address the complex challenges facing our society and to drive innovation in various fields. By investing in the necessary research, education, and resources, we can unlock the full potential of machine learning and create a future where decisions are informed by accurate predictive models and repetitive tasks are automated, freeing up time. The latter can be used for activities with higher added value. In this chapter, we investigate a modern development in learning systems with the creation of federated learning (FL) and its application to Internet of Things (IoT) domains. Federated learning forces some computation to devices themselves, thereby limiting the amount of data that needs to be transmitted to central servers for computation.