Time-series forecasting is predicting future values based on historical data. Applications include forecasting traffic flows, stock market trends, and energy consumption, which significantly helps to reduce costs and efficiency. However, the complexity inherent in time-series data makes accurate forecasting challenging. This article proposes a novel privacy-enhancing k-anonymous federated learning framework for time-series prediction based on microaggregation. This adaptable framework can be customised based on the client-side processing capabilities. We evaluate the performance of our proposed framework by comparing it with the centralized one using the standard metrics like Mean Absolute Error on three real-world datasets. Moreover, we performed a detailed ablation study by experimenting with different values of k in microaggregation and different client side forecasting models. The results show that our approach gives comparable a good privacyutility tradeoff as compared to the centralized benchmark.