Federated Learning is a machine learning technique to train a global model from different clients or devices while keeping the data decentralized.  A central server is in charge of coordinating the transfer of the models’ data between the server and the clients. It will update the global model while keeping the data private, since the data will never leave the device.

How is this connected to edge machine learning? The common use case for federated learning tackles the problem of incrementally learning from data directly on the device. An individual model is created for each device that works very accurately for that specific machine. What if we have many devices of the same type, that are operating under different or similar conditions? Is there a way to make each model smarter, more general, without losing its uniqueness, and being private-aware? That is the idea behind federated learning. To keep each model on each device unique for those conditions, while learning from the general characteristics of the other models. All of this, while keeping the data private on the device. 

Federated Learning

At Ekkono our goal is to always provide the best, most accurate, and most sustainable solution in the IoT market. Our research efforts in Federated Learning encompass building lightweight algorithms that can run on any platform, from industrial machinery to microcontrollers. We can do this without of a robust connection between the device and the cloud. This means that the models are both general and personalized for every device under different conditions. 

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