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This survey explores the domain of FML by analyzing the key motivations for FML and suggesting a unique taxonomy of FML techniques.
Aug 1, 2024 · This paper highlights their key ideas and envisions promising future trajectories of research, specifically discussing Federated Meta Knowledge ...
Nov 7, 2023 · In this tutorial, we present a comprehensive review of FL, meta learning, and federated meta learning (FedMeta). Unlike other tutorial papers, ...
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Federated learning aims to train a centralized model using the data stored in multiple distributed nodes in a privacy-aware manner [52]. Federated Averaging [53] ...
Sep 7, 2024 · In this work, we present a federated meta-learning framework for recommendation in which user information is shared at the level of algorithm, ...
Dec 23, 2023 · We propose a novel framework that combines federated learning with meta-learning techniques to enhance both efficiency and generalization ...
A novel FL framework called federated meta-learning based on fine-grained classifier reconstruction (FedFGCR) is presented in this paper.
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In this work, we present a federated meta-learning framework for recom- mendation in which user information is shared at the level of algorithm, instead of ...
It introduces a novel paradigm that makes global models personalized within fewer fine-tuning steps on the local dataset. This survey explores the domain of FML ...
This tutorial presents a comprehensive review of FL, meta learning, and federated meta learning (FedMeta), and their applications over wireless networks.