Federated Foundation Model

Federated Foundation Models (FFMs) combine the power of large, pre-trained foundation models with the privacy-preserving capabilities of federated learning, enabling collaborative model training across decentralized datasets without direct data sharing. Current research focuses on adapting various foundation model architectures, including transformers and convolutional neural networks, to federated settings, often employing techniques like personalized federated training and efficient model compression to address challenges related to communication overhead and resource heterogeneity. This approach holds significant promise for advancing fields like healthcare and time series forecasting by enabling the development of powerful, privacy-respecting models trained on diverse, geographically distributed data.