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- Robust Federated Learning with Noisy and Heterogeneous Clients
This paper starts the first attempt to study a new and challenging robust federated learning problem with noisy and heterogeneous clients We present a novel solution RHFL (Robust Heterogeneous Federated Learning), which simultaneously handles the label noise and performs federated learning in a single framework
- Noise-Robust Federated Learning With Model Heterogeneous Clients
Abstract: Federated Learning (FL) enables multiple devices to collaboratively train models without sharing their raw data Considering that clients may prefer to design their own models independently, model heterogeneous FL has emerged
- Learning Cautiously in Federated Learning with Noisy and Heterogeneous . . .
The co-existence of label noise and class imbalance in FL’s small local datasets renders conventional FL methods and noisy-label learning methods both ineffective To address the challenges, we propose FEDCNI without using an additional clean proxy dataset
- Robust Federated Learning with Real‐World Noisy Data
Experiments conducted on CIFAR‐10 100 with federated synthetic label noise, and on a real‐world noisy dataset, Clothing1M, demonstrate that FedCorr is robust to label noise and substantially outperforms the state‐of‐the‐art methods at multiple noise levels
- Robust Federated Learning for Heterogeneous Clients and Unreliable . . .
Abstract: Federated Learning (FL) serves as a machine learning paradigm where distributed devices collaboratively train on local data, with their models subsequently aggregated on a central server However, challenges arise due to unreliable communication channels, potential sign errors in model parameters, data heterogeneity, and resource
- Robust Asymmetric Heterogeneous Federated Learning With Corrupted Clients
To address these issues, this paper introduces a novel Robust Asymmetric Heterogeneous Federated Learning (RAHFL) framework We propose a Diversity-enhanced supervised Contrastive Learning technique to enhance the resilience and adaptability of local models on various data corruption patterns
- Robust Model Aggregation for Federated Learning with Heterogeneous Clients
In this paper, we propose an aggregation algorithm considering the accuracy of local model of heterogeneous clients The server evaluates the accuracy of the uploaded local model with a benchmark dataset, and then updates model parameters according to the accuracy ratio
- Advances in Robust Federated Learning: A Survey With Heterogeneity . . .
In this paper, we first outline the basic concepts of heterogeneous FL and summarize the research challenges in FL in terms of five aspects: data, model, task, device and communication In addition, we explore how existing state-of-the-art approaches cope with the heterogeneity of FL, and categorize and review these approaches at three
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