Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources

• Published in: Journal of parallel and distributed computing Vol. 203; p. 105103
• Main Authors: Banerjee, Roopkatha, Modi, Prince, Vyas, Jinal, Sri Abhijit, Chunduru, Chandrashekar, Tejus, Marisetty, Harsha Varun, Gupta, Manik, Simmhan, Yogesh
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2025
• Subjects: Distributed programming; Edge intelligence; Federated learning; Federated learning; Distributed programming; Edge intelligence
• ISSN: 0743-7315
• DOI: 10.1016/j.jpdc.2025.105103

With the recent improvements in mobile and edge computing and rising concerns of data privacy, Federated Learning (FL) has rapidly gained popularity as a privacy-preserving, distributed machine learning methodology. Several FL frameworks have been built for testing novel FL strategies. However, most...