What is federated learning? - IBM Research
Aug 24, 2022 · New AI models are being trained collaboratively on the edge, on data that never leave your mobile phone, laptop, or private server. This new form of AI training is called federated learning, …
Is Federated Learning Still Alive in the Foundation Model Era?
Mar 25, 2024 · Is Federated Learning Still Alive in the Foundation Model Era? Abstract Federated learning (FL) has arisen as an alternative to collecting large amounts of data in a central place to …
Privacy-Preserving Personalized Federated Prompt Learning for ...
Apr 24, 2025 · Federated Prompt Learning (FPL) is a recently proposed approach that combines pre-trained multimodal LLMs such as vision-language models with federated learning to create …
Federated Learning meets Homomorphic Encryption - IBM Research
Dec 16, 2022 · Federated learning not only improves model generalization, but also opens new doors for enterprises to train machine learning models that extract knowledge of training data that cannot be …
Federated Learning While Providing Model as a Service: Joint Training ...
May 20, 2024 · Federated learning (FL) is beneficial for enabling the training of models across distributed clients while keeping the data locally. However, existing work has overlooked the …
Model Pruning Enables Efficient Federated Learning on Edge Devices
Dec 31, 2021 · Federated learning (FL) allows model training from local data collected by edge/mobile devices while preserving data privacy, which has wide applicability to image and vision applications.
Adaptive Model Pruning for Hierarchical Wireless Federated Learning
Apr 21, 2024 · Federated Learning (FL) is a promising privacy-preserving distributed learning framework where a server aggregates models updated by multiple devices without accessing their private …
Federated Systems - IBM Research
IBMFL is a python framework developed to enable federated learning in an enterprise environment. It provides a basic fabric for FL on which advanced features can be added.
Flick: Empowering Federated Learning with Commonsense Knowledge
5 days ago · Inspired by the data-driven approach, we propose Flick, a novel data generation framework for heterogeneous Federated Learning with Commonsense Knowledge from Large Language …
Building privacy-preserving federated learning to help fight financial ...
May 5, 2023 · We developed a solution to tackle the problem of enabling federated learning over a large set of shared data — while preserving data privacy with the goal of improving the detection of …