Federated Learning with New Knowledge: Fundamentals, Advances, and Futures
Not on the map. Why?
Abstract (arXiv)
Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly developing in an era where privacy protection is increasingly valued. It is this rapid development trend, along with the continuous emergence of new demands for FL in the real world, that prompts us to focus on a very important problem: Federated Learning with New Knowledge. The primary challenge here is to effectively incorporate various new knowledge into existing FL systems and evolve these systems to reduce costs, extend their lifespan, and facilitate sustainable development. In this paper, we systematically define the main sources of new knowledge in FL, including new features, tasks, models, and algorithms. For each source, we thoroughly analyze and discuss how to incorporate new knowledge into existing FL systems and examine the impact of the form and timing of new knowledge arrival on the incorporation process. Furthermore, we comprehensively discuss the potential future directions for FL with new knowledge, considering a variety of factors such as scenario setups, efficiency, and security. There is also a continuously updating repository for this topic: https://github.com/conditionWang/FLNK.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 50 of its 70 references that are in the map. First posted on arXiv 3 Feb 2024.
Builds on
50 of 70 references.
References · 50
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2019Advances and Open Problems in Federated Learningcited by 3,021
- 2018Federated Optimization in Heterogeneous Networkscited by 2,971
- 2021Model-Contrastive Federated Learningcited by 762
- 2017Federated Multi-Task Learningcited by 669
- 2020Ditto: Fair and Robust Federated Learning Through Personalizationcited by 665
- 2020Personalized Federated Learning with Moreau Envelopescited by 612
- 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
- 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 284
- 2020Personalized Cross-Silo Federated Learning on Non-IID Datacited by 282
- 2021Evaluating Gradient Inversion Attacks and Defenses in Federated Learningcited by 196
- 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 160
38 more
- 2020Federated Continual Learning with Weighted Inter-client Transfercited by 158
- 2019Federated Adversarial Domain Adaptationcited by 140
- 2019Overcoming Forgetting in Federated Learning on Non-IID Datacited by 120
- 2020Feature Inference Attack on Model Predictions in Vertical Federated Learningcited by 105
- 2022FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extractioncited by 102
- 2023When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directionscited by 83
- 2022Divergence-aware Federated Self-Supervised Learningcited by 71
- 2019Variational Federated Multi-Task Learningcited by 69
- 2023Better Generative Replay for Continual Federated Learningcited by 53
- 2023Federated Continual Learning via Knowledge Fusion: A Surveycited by 53
- 2023Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Handcited by 52
- 2020What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentationcited by 50
- 2021Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorizationcited by 50
- 2021Federated Multi-Target Domain Adaptationcited by 44
- 2021A distillation-based approach integrating continual learning and federated learning for pervasive servicescited by 42
- 2022Federated Domain Generalization for Image Recognition via Cross-Client Style Transfercited by 40
- 2023Federated Incremental Semantic Segmentationcited by 36
- 2020Continual Local Training for Better Initialization of Federated Modelscited by 34
- 2021CaPC Learning: Confidential and Private Collaborative Learningcited by 33
- 2023Can Public Large Language Models Help Private Cross-device Federated Learning?cited by 30
- 2019Eavesdrop the Composition Proportion of Training Labels in Federated Learningcited by 27
- 2021SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervisioncited by 26
- 2023Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teachercited by 25
- 2023Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learningcited by 21
- 2022PolicyCleanse: Backdoor Detection and Mitigation in Reinforcement Learningcited by 18
- 2022Feature Distribution Matching for Federated Domain Generalizationcited by 18
- 2022Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identificationcited by 13
- 2023DEJA VU: Continual Model Generalization For Unseen Domainscited by 13
- 2023Mutual Enhancement of Large and Small Language Models with Cross-Silo Knowledge Transfercited by 13
- 2024Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalizationcited by 12
- 2021Self-supervised Cross-silo Federated Neural Architecture Searchcited by 11
- 2022Federated Continual Learning for Text Classification via Selective Inter-client Transfercited by 9
- 2021Weak Adaptation Learning -- Addressing Cross-domain Data Insufficiency with Weak Annotatorcited by 8
- 2021Privacy-Preserving Constrained Domain Generalization via Gradient Alignmentcited by 8
- 2022Anomaly Detection through Unsupervised Federated Learningcited by 8
- 2022Federated Unsupervised Domain Adaptation for Face Recognitioncited by 6
- 2023Continual Adaptation of Vision Transformers for Federated Learningcited by 6
- 2023Federated Continual Novel Class Learningcited by 4
Built on by
Not cited yet.