🤝 Federated Learning

📷 CVPR2026 · 4 paper notes

🔥 Top topics: Federated Learning ×3 · Personalized Generation ×2

Fully Decentralized Certified Unlearning

Addressing the neglected scenario of "decentralized networks without a central coordinator," this paper proposes RR-DU—a random-walk-based certified unlearning algorithm. It performs noisy projected gradient ascent on the forgetting set only at the client initiating the deletion, while other clients continue with noise-free descent. By incorporating sub-sampled Gaussian noise and trust region projections, the authors prove \((\varepsilon,\delta)\) network unlearning certificates, convergence, and deletion capacity bounds. Notably, the noise does not scale with the size of the forgetting set \(m\), successfully reducing backdoor attack success rates to random-guess levels while maintaining clean accuracy on image classification tasks.

GDFA: Geometry-Driven Federated Unlearning with Directional Task Vector Alignment

GDFA reinterprets "Federated Unlearning" as a loss surface geometry problem: it first migrates the global model to a flat minima region via perturbations, then has relevant clients generate task vectors on unlearning data, retaining only components with directional consensus (sign consensus) for reverse aggregation. This achieves precise erasure of target client knowledge in Non-IID scenarios with almost no loss in retention task accuracy.

HiLoRA: Hierarchical Low-Rank Adaptation for Personalized Federated Learning

HiLoRA decomposes the LoRA update of each client into a three-layer orthogonal subspace structure consisting of "root-cluster-leaf," which respectively capture global consensus, subgroup commonalities, and client personalization. Combined with an adaptive clustering mechanism based on LoRA subspace similarity, it achieves SOTA performance in both personalization and generalization to new clients on CIFAR-100 and DomainNet.

Personalized Federated Training of Diffusion Models with Privacy Guarantees

PFDM decomposes the reverse denoising process of diffusion models into two components: a "client-private denoiser + server-shared denoiser." Clients only upload data that has been clipped and subjected to forward noise, providing formal Local Differential Privacy (LDP) guarantees for each data point. The shared model only observes noised data and cannot reproduce any client samples in isolation, while collaboration significantly enhances generation quality for minority or underrepresented classes.