fluke: Federated Learning Utility frameworK for Experimentation and research
원문 초록
Since its inception in 2016, Federated Learning (FL) has been gaining tremendous popularity in the machine learning community. Several frameworks have been proposed to facilitate the development of FL algorithms, but researchers often resort to implementing their algorithms from scratch, including all baselines and experiments. This is because existing frameworks are not flexible enough to support their needs or the learning curve to extend them is too steep. In this paper, we present \fluke, a Python package designed to simplify the development of new FL algorithms. fluke is specifically designed for prototyping purposes and is meant for researchers or practitioners focusing on the learning components of a federated system. fluke is open-source, and it can be either used out of the box or extended with new algorithms with minimal overhead.
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FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning
Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
Prototype-Guided and Lightweight Adapters for Inherent Interpretation and Generalisation in Federated Learning
FedPall: Prototype-based Adversarial and Collaborative Learning for Federated Learning with Feature Drift
BackFed: An Efficient & Standardized Benchmark Suite for Backdoor Attacks in Federated Learning
토론
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