Jul 2026· IEEE Transactions on Pattern Analysis and Machine Intelligence· Vol PP, pp. 1-17· 0 citations
Medicine
TL;DR
The proposed FedMGP+ effectively mitigates forgetting, significantly enhances personalized performance, and provides robust defense against poisoning and gradient leakage attacks.
Abstract
Personalized Federated Continual Learning (PFCL) requires that the server not only effectively integrate temporal knowledge accumulated across past tasks and spatial knowledge distributed among heterogeneous clients, but also ensure strong personalized performance of the global model for each client. Existing methods, whether in Personalized Federated Learning (PFL) or Federated Continual Learning (FCL), have overlooked the multi-granularity representation of knowledge, which can be utilized to overcome Spatial-Temporal Catastrophic Forgetting (STCF) and enable coarse-to-fine personalization. Furthermore, most approaches rely on local client-side personalization, increasing computational load and failing to address the risks posed by malicious or low-quality clients. To this end, we propose FedMGP+, which utilizes multi-granularity prompts to address these challenges, namely coarse-grained global prompt and fine-grained local prompt. The former focuses on efficiently transferring shared global knowledge without spatial forgetting, and the latter emphasizes specific learning of personalized local knowledge to overcome temporal forgetting. Visualization results and theoretical analyses further reveal that coarse-grained prompts primarily guide regional attention, whereas fine-grained prompts enrich object-level representations within those regions. Building upon this, Personalized Selective Prompt Fusion is designed to exclusively fuse coarse-grained knowledge on the server to generate client-specific global prompts, reducing local overhead and resisting poisoning attacks from malicious clients. Extensive experiments demonstrate that the proposed FedMGP+ effectively mitigates forgetting, significantly enhances personalized performance, and provides robust defense against poisoning and gradient leakage attacks.
Results indicate that multi-factor reliability assessment and heterogeneity-aware adaptive forgetting improve the balance between useful knowledge retention and harmful-knowledge suppression in heterogeneous FCL.
Federated Continual Multimodal Learning (FedCMM), a framework that embeds continual-learning safeguards into the federated optimization loop at three complementary levels, is proposed, confirming that holistic, modality-aware optimization enables robust evolutive adaptation across heterogeneous networked AI deployments.
Federated Graph Learning (FGL) enables privacy-preserving GNN training over distributed graph data, yet dynamic task streams in Federated Graph Continual Learning (FGCL) inevitably lead to catastrophic forgetting. From a spectral perspective, this forgetting manifests as two fundamental challenges: high-frequency inconsistency forgetting, where newly emerging node inconsistencies disrupt message passing and erase discriminative knowledge, and low-frequency consistency forgetting, where over-adaptation to new tasks dilutes global semantic coherence. Existing FGCL methods fail to explicitly address these dual spectral issues, resulting in severe degradation of knowledge retention and task generalization across continual learning stages. To this end, we propose FedFST, a spectral-aware framework that mitigates dual spectral forgetting. FedFST comprises Historical High-Frequency Knowledge Restoration (HHKR) to reconstruct and preserve high-frequency inconsistency knowledge, and Historical Low-Frequency Semantic Transfer (HLST) to stabilize low-frequency consistency via spectral distillation. Extensive experiments demonstrate the effectiveness of FedFST in alleviating catastrophic forgetting in FGCL. The code is available at https://github.com/YunQi572/FedFST.git.
Hanyao Guo, Zihan Tan, Wenke Huang et al.· Proceedings of the 32nd ACM...· 0 citations
FedADB, a Class Anchor-Driven Dual-Branch FL framework, a dual-branch collaborative training mechanism designed for clients that achieves significant improvements in both accuracy and convergence speed.
Zhenyan Liu, Hua Zhang, Haoran Gao et al.· 0 citations
A novel FL framework is presented, FedPhoenix, that stochastically re-sets partial parameters in each round to destroy some features of the global model, guiding FL training to learn multiple generalized features for inference rather than specific overfitting features.
Jia-Hao Wu, Ming Hu, Yanxin Yang et al.· Neural Information Processin...· 1 citation
Prototype-based knowledge sharing effectively mitigates data and model heterogeneity in federated learning (FL) by exchanging class-level semantic information. However, existing methods typically assume all local prototypes are equally reliable. Consequently, low-quality prototypes from heterogeneous models or dynamic clients can contaminate the global aggregation, leading to a vicious cycle of noise accumulation and performance degradation. To address this, we propose FedLEAF, a Federated Learning framework with server-side proactive Evaluation and clientside Adaptive Fusion. Specifically, the server employs an Adaptive Learning Prototypes (ALP) network to dynamically evaluate prototype reliability and generate learnable aggregation weights, ensuring that highquality prototypes exert a primary influence on the global model. Meanwhile, the client utilizes a Historical Consistency Fusion (HCF) strategy to selectively absorb global knowledge by assessing its consistency with locally maintained historical prototypes. Extensive experiments on standard datasets demonstrate that FedLEAF achieves effective improvements in model accuracy and robustness compared to existing methods.
Zhiyuan Zhu, Si-Yi Deng, Dapeng Wu et al.· 2026 International Conferenc...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
