# Gains: Fine-grained Federated Domain Adaptation in Open Set

arXiv: https://arxiv.org/abs/2510.15967 · HTML: https://mlsanity.com/paper/2510.15967/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 67 references that are in the map.
- First posted on arXiv 12 Oct 2025.

## Builds on (most cited first, 30 of 33)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [FedProto: Federated Prototype Learning across Heterogeneous Clients](https://mlsanity.com/paper/2105.00243/) (2021)
- [Transferrable Prototypical Networks for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1904.11227/) (2019)
- [A Comprehensive Survey on Source-free Domain Adaptation](https://mlsanity.com/paper/2302.11803/) (2023)
- [Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptation](https://mlsanity.com/paper/2204.03838/) (2022)
- [Recent Advances on Federated Learning: A Systematic Survey](https://mlsanity.com/paper/2301.01299/) (2023)
- [Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer](https://mlsanity.com/paper/2210.00912/) (2022)
- [Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning](https://mlsanity.com/paper/2210.02326/) (2022)
- [Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables](https://mlsanity.com/paper/2008.09469/) (2020)
- [Towards Efficient Replay in Federated Incremental Learning](https://mlsanity.com/paper/2403.05890/) (2024)
- [Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity](https://mlsanity.com/paper/2405.16585/) (2024)
- [Domain Generalization through Meta-Learning: A Survey](https://mlsanity.com/paper/2404.02785/) (2024)
- [Federated Domain Generalization: A Survey](https://mlsanity.com/paper/2306.01334/) (2023)
- [Personalized Federated Domain-Incremental Learning based on Adaptive Knowledge Matching](https://mlsanity.com/paper/2407.05005/) (2024)
- [Model Predictive Task Sampling for Efficient and Robust Adaptation](https://mlsanity.com/paper/2501.11039/) (2025)
- [Personalized Federated Continual Learning via Multi-granularity Prompt](https://mlsanity.com/paper/2407.00113/) (2024)
- [Ten Challenging Problems in Federated Foundation Models](https://mlsanity.com/paper/2502.12176/) (2025)
- [Collaborative Semantic Aggregation and Calibration for Federated Domain Generalization](https://mlsanity.com/paper/2110.06736/) (2021)
- [Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter](https://mlsanity.com/paper/2502.20709/) (2025)
- [Structural features of the fly olfactory circuit mitigate the stability-plasticity dilemma in continual learning](https://mlsanity.com/paper/2502.01427/) (2025)
- [Robust Fast Adaptation from Adversarially Explicit Task Distribution Generation](https://mlsanity.com/paper/2407.19523/) (2024)
- [Unsupervised Video Domain Adaptation with Masked Pre-Training and Collaborative Self-Training](https://mlsanity.com/paper/2312.02914/) (2023)
- [Privacy-Preserving Constrained Domain Generalization via Gradient Alignment](https://mlsanity.com/paper/2105.08511/) (2021)
- [SacFL: Self-Adaptive Federated Continual Learning for Resource-Constrained End Devices](https://mlsanity.com/paper/2505.00365/) (2025)
- [Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting](https://mlsanity.com/paper/2302.05049/) (2023)
- [Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning](https://mlsanity.com/paper/2410.07738/) (2024)
- [StatMix: Data augmentation method that relies on image statistics in federated learning](https://mlsanity.com/paper/2207.04103/) (2022)

## Abstract (arXiv)

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.