Gains: Fine-grained Federated Domain Adaptation in Open Set

• URL: http://arxiv.org/abs/2510.15967v1
• Date: Sun, 12 Oct 2025 13:38:11 GMT
• Title: Gains: Fine-grained Federated Domain Adaptation in Open Set
• Authors: Zhengyi Zhong, Wenzheng Jiang, Weidong Bao, Ji Wang, Cheems Wang, Guanbo Wang, Yongheng Deng, Ju Ren,
• Abstract summary: New clients continuously join the FL process, introducing new knowledge.<n>Existing research focuses on coarse-grained knowledge discovery.<n>We propose a fine-grained federated domain adaptation approach in open set (Gains)
• Score: 14.04994851668009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional federated learning (FL) assumes a closed world with a fixed total number of clients. In contrast, new clients continuously join the FL process in real-world scenarios, introducing new knowledge. This raises two critical demands: detecting new knowledge, i.e., knowledge discovery, and integrating it into the global model, i.e., knowledge adaptation. Existing research focuses on coarse-grained knowledge discovery, and often sacrifices source domain performance and adaptation efficiency. To this end, we propose a fine-grained federated domain adaptation approach in open set (Gains). Gains splits the model into an encoder and a classifier, empirically revealing features extracted by the encoder are sensitive to domain shifts while classifier parameters are sensitive to class increments. Based on this, we develop fine-grained knowledge discovery and contribution-driven aggregation techniques to identify and incorporate new knowledge. Additionally, an anti-forgetting mechanism is designed to preserve source domain performance, ensuring balanced adaptation. Experimental results on multi-domain datasets across three typical data-shift scenarios demonstrate that Gains significantly outperforms other baselines in performance for both source-domain and target-domain clients. Code is available at: https://github.com/Zhong-Zhengyi/Gains.

Related papers

• Rethinking the Backbone in Class Imbalanced Federated Source Free Domain Adaptation: The Utility of Vision Foundation Models [9.432150710329609]
  Federated Learning (FL) offers a framework for training models collaboratively while preserving data privacy of each client.<n>We propose replacing the FFREEDA backbone with a frozen vision foundation model (VFM)<n>VFMs effectively mitigate the effects of domain gaps, class imbalances, and even non-IID-ness among target clients.
  arXiv  Detail & Related papers  (2025-09-10T08:10:57Z)
• Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement [9.87623531653534]
  Graph foundation models (GFM) offer strong domain generalization but are usually trained on single machines.<n>We propose FedGFM, a novel decentralized GFM training paradigm.<n>Key challenge is knowledge entanglement, where multi-domain knowledge merges into indistinguishable representations.<n>We present FedGFM+, an enhanced framework with two core modules to reduce knowledge entanglement.
  arXiv  Detail & Related papers  (2025-05-19T04:06:32Z)
• Federated Learning with Domain Shift Eraser [16.685557256108265]
  Federated learning (FL) is emerging as a promising technique for collaborative learning without local data leaving their devices.<n>We propose a novel FL framework, Federated Domain Shift Eraser (FDSE), to improve model performance.
  arXiv  Detail & Related papers  (2025-03-17T11:10:31Z)
• Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting [10.061328213032088]
  Federated Class-Incremental Learning (FCIL) refers to a scenario where a dynamically changing number of clients collaboratively learn an ever-increasing number of incoming tasks.<n>We develop a mathematical framework for FCIL that formulates local and global forgetting.<n>We propose an approach called Hybrid Rehearsal which utilizes latent exemplars and data-free techniques to address local and global forgetting.
  arXiv  Detail & Related papers  (2025-01-26T01:08:01Z)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• 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)
• Momentum Benefits Non-IID Federated Learning Simply and Provably [22.800862422479913]
  Federated learning is a powerful paradigm for large-scale machine learning. FedAvg and SCAFFOLD are two prominent algorithms to address these challenges. This paper explores the utilization of momentum to enhance the performance of FedAvg and SCAFFOLD.
  arXiv  Detail & Related papers  (2023-06-28T18:52:27Z)
• Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework [82.36466358313025]
  We propose a primal-dual FL algorithm, termed FedVRA, that allows one to adaptively control the variance-reduction level and biasness of the global model. Experiments based on (semi-supervised) image classification tasks demonstrate superiority of FedVRA over the existing schemes.
  arXiv  Detail & Related papers  (2022-12-03T03:27:51Z)
• Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning [122.62311703151215]
  Divide and Contrast (DaC) aims to connect the good ends of both worlds while bypassing their limitations. DaC divides the target data into source-like and target-specific samples, where either group of samples is treated with tailored goals. We further align the source-like domain with the target-specific samples using a memory bank-based Maximum Mean Discrepancy (MMD) loss to reduce the distribution mismatch.
  arXiv  Detail & Related papers  (2022-11-12T09:21:49Z)
• Unsupervised Domain Adaptation via Style-Aware Self-intermediate Domain [52.783709712318405]
  Unsupervised domain adaptation (UDA) has attracted considerable attention, which transfers knowledge from a label-rich source domain to a related but unlabeled target domain.<n>We propose a novel style-aware feature fusion method (SAFF) to bridge the large domain gap and transfer knowledge while alleviating the loss of class-discnative information.
  arXiv  Detail & Related papers  (2022-09-05T10:06:03Z)
• Multi-Source Domain Adaptation Based on Federated Knowledge Alignment [0.0]
  Federated Learning (FL) facilitates distributed model learning to protect users' privacy. We propose Federated Knowledge Alignment (FedKA) that aligns features from different clients and those of the target task.
  arXiv  Detail & Related papers  (2022-03-22T11:42:25Z)
• Federated and Generalized Person Re-identification through Domain and Feature Hallucinating [88.77196261300699]
  We study the problem of federated domain generalization (FedDG) for person re-identification (re-ID) We propose a novel method, called "Domain and Feature Hallucinating (DFH)", to produce diverse features for learning generalized local and global models. Our method achieves the state-of-the-art performance for FedDG on four large-scale re-ID benchmarks.
  arXiv  Detail & Related papers  (2022-03-05T09:15:13Z)

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.