Time Series data has become present everywhere, thanks to affordable edge devices and sensors. Much of this data is valuable for decision making. To use this data for the forecasting task, the conventional centralized approach has shown deficiencies regarding extensive data communication and data privacy issues. Federated learning allows learning from decentralized data without the need to store it centrally. The data remains where it was generated, which guarantees privacy and reduces communication costs [1]. FL already naturally selects only a few nodes randomly at each round. They have non-iid data and also varying in amount. After some iteration of training, the central aggregator will generate a global model.