HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images

• URL: http://arxiv.org/abs/2112.10775v1
• Date: Mon, 20 Dec 2021 13:25:48 GMT
• Title: HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images
• Authors: Meirui Jiang, Zirui Wang, Qi Dou
• Abstract summary: We introduce a new framework called HarmoFL to handle both local and global drifts. HarmoFL mitigates the local update drift by normalizing amplitudes of images transformed into the frequency domain. We show that HarmoFL outperforms a set of recent state-of-the-art methods with promising convergence behavior.
• Score: 19.62267284815759
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiple medical institutions collaboratively training a model using federated learning (FL) has become a promising solution for maximizing the potential of data-driven models, yet the non-independent and identically distributed (non-iid) data in medical images is still an outstanding challenge in real-world practice. The feature heterogeneity caused by diverse scanners or protocols introduces a drift in the learning process, in both local (client) and global (server) optimizations, which harms the convergence as well as model performance. Many previous works have attempted to address the non-iid issue by tackling the drift locally or globally, but how to jointly solve the two essentially coupled drifts is still unclear. In this work, we concentrate on handling both local and global drifts and introduce a new harmonizing framework called HarmoFL. First, we propose to mitigate the local update drift by normalizing amplitudes of images transformed into the frequency domain to mimic a unified imaging setting, in order to generate a harmonized feature space across local clients. Second, based on harmonized features, we design a client weight perturbation guiding each local model to reach a flat optimum, where a neighborhood area of the local optimal solution has a uniformly low loss. Without any extra communication cost, the perturbation assists the global model to optimize towards a converged optimal solution by aggregating several local flat optima. We have theoretically analyzed the proposed method and empirically conducted extensive experiments on three medical image classification and segmentation tasks, showing that HarmoFL outperforms a set of recent state-of-the-art methods with promising convergence behavior.

Related papers

• Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates [71.81037644563217]
  Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. We propose straggler-aware layer-wise federated learning (SALF) that leverages the optimization procedure of NNs via backpropagation to update the global model in a layer-wise fashion.
  arXiv  Detail & Related papers  (2024-03-27T09:14:36Z)
• Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding [21.76802204235636]
  We propose representation encoding-based federated meta-learning (REFML) for few-shot fault diagnosis. REFML harnesses the inherent generalization among training clients, effectively transforming it into an advantage for out-of-distribution. It achieves an increase in accuracy by 2.17%-6.50% when tested on unseen working conditions of the same equipment type and 13.44%-18.33% when tested on totally unseen equipment types.
  arXiv  Detail & Related papers  (2023-10-13T10:48:28Z)
• Rethinking Client Drift in Federated Learning: A Logit Perspective [125.35844582366441]
  Federated Learning (FL) enables multiple clients to collaboratively learn in a distributed way, allowing for privacy protection. We find that the difference in logits between the local and global models increases as the model is continuously updated. We propose a new algorithm, named FedCSD, a Class prototype Similarity Distillation in a federated framework to align the local and global models.
  arXiv  Detail & Related papers  (2023-08-20T04:41:01Z)
• FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation [32.36334319329364]
  Cross-silo federated learning (FL) enables the development of machine learning models on datasets distributed across data centers. Recent research has found that current FL algorithms face a trade-off between local and global performance when confronted with distribution shifts. We propose a novel federated model soup method to optimize the trade-off between local and global performance.
  arXiv  Detail & Related papers  (2023-07-20T00:07:29Z)
• Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape [59.841889495864386]
  In federated learning (FL), a cluster of local clients are chaired under the coordination of a global server. Clients are prone to overfit into their own optima, which extremely deviates from the global objective. ttfamily FedSMOO adopts a dynamic regularizer to guarantee the local optima towards the global objective. Our theoretical analysis indicates that ttfamily FedSMOO achieves fast $mathcalO (1/T)$ convergence rate with low bound generalization.
  arXiv  Detail & Related papers  (2023-05-19T10:47:44Z)
• Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination [33.12164201146458]
  We propose a novel and FL paradigm named FedMR (Federated Model Recombination) The goal of FedMR is to guide the recombined models to be trained towards a flat area. Compared with state-of-the-art FL methods, FedMR can significantly improve the inference accuracy without exposing privacy of each client.
  arXiv  Detail & Related papers  (2023-05-18T05:58:24Z)
• FedMR: Fedreated Learning via Model Recombination [7.404225808071622]
  Federated Learning (FL) enables global model training across clients without compromising their confidential local data. Existing FL methods rely on Federated Averaging (FedAvg)-based aggregation. This paper proposes a novel and effective FL paradigm named FedMR (Federating Model Recombination)
  arXiv  Detail & Related papers  (2022-08-16T11:30:19Z)
• DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup [58.894901088797376]
  federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. We propose a general framework to solve the above two challenges simultaneously. We provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability.
  arXiv  Detail & Related papers  (2022-04-16T08:08:29Z)
• Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning [86.59588262014456]
  Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. We propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG) Our FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and SCAFFOLD.
  arXiv  Detail & Related papers  (2022-03-17T11:18:17Z)
• Local Adaptivity in Federated Learning: Convergence and Consistency [25.293584783673413]
  Federated learning (FL) framework trains a machine learning model using decentralized data stored at edge client devices by periodically aggregating locally trained models. We show in both theory and practice that while local adaptive methods can accelerate convergence, they can cause a non-vanishing solution bias. We propose correction techniques to overcome this inconsistency and complement the local adaptive methods for FL.
  arXiv  Detail & Related papers  (2021-06-04T07:36:59Z)
• A Bayesian Federated Learning Framework with Online Laplace Approximation [144.7345013348257]
  Federated learning allows multiple clients to collaboratively learn a globally shared model. We propose a novel FL framework that uses online Laplace approximation to approximate posteriors on both the client and server side. We achieve state-of-the-art results on several benchmarks, clearly demonstrating the advantages of the proposed method.
  arXiv  Detail & Related papers  (2021-02-03T08:36:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.