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Heterogeneous federated learning: State-of-the-art and research challenges
Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …
scale industrial applications. Existing FL works mainly focus on model homogeneous …
Enhancing generalization in federated learning with heterogeneous data: A comparative literature review
A Mora, A Bujari, P Bellavista - Future Generation Computer Systems, 2024 - Elsevier
Federated Learning (FL) is a collaborative training paradigm whereby a global Machine
Learning (ML) model is trained using typically private and distributed data sources without …
Learning (ML) model is trained using typically private and distributed data sources without …
Federated learning for generalization, robustness, fairness: A survey and benchmark
Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …
collaboration among different parties. Recently, with the popularity of federated learning, an …
Federated domain generalization with generalization adjustment
Abstract Federated Domain Generalization (FedDG) attempts to learn a global model in a
privacy-preserving manner that generalizes well to new clients possibly with domain shift …
privacy-preserving manner that generalizes well to new clients possibly with domain shift …
Make landscape flatter in differentially private federated learning
To defend the inference attacks and mitigate the sensitive information leakages in Federated
Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard for privacy …
Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard for privacy …
Improving generalization in federated learning by seeking flat minima
D Caldarola, B Caputo, M Ciccone - European Conference on Computer …, 2022 - Springer
Abstract Models trained in federated settings often suffer from degraded performances and
fail at generalizing, especially when facing heterogeneous scenarios. In this work, we …
fail at generalizing, especially when facing heterogeneous scenarios. In this work, we …
Improving the model consistency of decentralized federated learning
To mitigate the privacy leakages and communication burdens of Federated Learning (FL),
decentralized FL (DFL) discards the central server and each client only communicates with …
decentralized FL (DFL) discards the central server and each client only communicates with …
Dynamic regularized sharpness aware minimization in federated learning: Approaching global consistency and smooth landscape
In federated learning (FL), a cluster of local clients are chaired under the coordination of the
global server and cooperatively train one model with privacy protection. Due to the multiple …
global server and cooperatively train one model with privacy protection. Due to the multiple …
Federated learning on non-iid graphs via structural knowledge sharing
Graph neural networks (GNNs) have shown their superiority in modeling graph data. Owing
to the advantages of federated learning, federated graph learning (FGL) enables clients to …
to the advantages of federated learning, federated graph learning (FGL) enables clients to …
Fedclip: Fast generalization and personalization for clip in federated learning
Federated learning (FL) has emerged as a new paradigm for privacy-preserving
computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …
computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …