Alzheimer’s, as a neurodegenerative disease, has many challenges due to the complexity of this disease, with big data and the necessity of analyzing them fully to reach a precise prediction. The main challenges to conventional centralized approaches involve privacy and data silos, which impede collaboration and fully analyzing these very extensive, heterogeneous data sets. FL thus turns out to be a promising solution for model training on decentralized data while keeping the data privately owned. An FL framework has been developed expressly to predict AD progression with high accuracy, which is of prime importance in modern healthcare. This framework utilizes FL to facilitate collaboration among multiple healthcare institutions, each of which possesses local datasets of AD patients. Through a distributed learning paradigm, models are trained locally on the data of each institution without the need for data sharing. In its place, the model updates are centrally aggregated for privacy protection, yet benefiting from the wisdom accumulated across all participating institutions. The process of FL works by model training and aggregation through iterations for the constant improvement of the models without breach in the security of the data. This architecture fuses state-of-the-art federated learning with deep learning algorithms to learn complex patterns from the data. Experimental validation on real-world datasets shows how the proposed FL can make accurate and reliable predictions of AD progression. Moreover, all the analyses provided here show that privacy and confidentiality in the FL process are assured. Overall, FL framework allows scalable, privacy-preserving AD prediction and, therefore, opens up ways toward collaborative research and personalized healthcare interventions against Alzheimer’s Disease.