This paper proposes a robust decentralized personalized federated learning method R-DPFL, that enables clients to reduce the impact of Byzantine attacks via robust neighborhood direction estimation and history-based update trend prediction, rather than purely aggregating client models as in the existing work. In R-DPFL, each client first computes the current-round model update by aggregating the received neighborhood update vectors. It then predicts what this update should be based on its historical values and local model changes. Finally, R-DPFL computes the difference between these two quantities, adaptively clips this difference, and adds it to the local update. We prove convergence of the learning process through rigorous analysis and show that honest clients maintain stable personalized descent dynamics under Byzantine neighbor perturbations without requiring consensus among neighboring models. Extensive experiments on CIFAR-10 demonstrate that RDPFL consistently outperforms state-of-the-art decentralized and personalized federated learning baselines under heterogeneous and adversarial settings.
This work proposes SAPE-FL (Similarity-Aware Personalized Federated Learning), a novel personalization framework that anchors each client's model to both the global model and a similarity-weighted peer averaged model that mitigates negative transfer and enhances robustness in heterogeneous settings.
V. ArunKumarA, Sunil Gupta, N. Dang et al.· 0 citations
This paper shows that the proposed method ensures a bounded Byzantine influence on both distillation gradients and individual client private gradients after cross-modality fusion, thereby enabling stable local optimization for honest clients under Byzantine distillation.
OrthoFL is proposed, an orthogonal calibration framework that decouples global and local learning progress to reduce interference and consistently outperforms state-of-the-art asynchronous baselines under various delay patterns and heterogeneity scenarios.
Jiayun Zhang, Shuheng Li, Haiyu Huang et al.· Proceedings of the 32nd ACM...· 0 citations
FedUP is an uncertainty-aware personalized federated learning framework that models prototypes as probability distributions and aggregates prototype distributions through reliability-informed barycenters on the statistical manifold, suppressing unreliable contributions while preserving geometric structure.
Fu Qi, Weishan Zhang, Ling-Zhao Meng et al.· Proceedings of the Thirty-Fi...· 0 citations
Local Inference Guided Aggregation for Heterogeneous Training Environments to Yield Enhancement Through Agreement and Regularization (LIGHTYEAR), a federated learning framework that performs update selection in function space using an NTK-based agreement score to characterize predictive behavior and determine a persona...
Mirko Konstantin, S. Zachow, Anirban Mukhopadhyay· 0 citations
GRAB-FL is proposed, a graph-aware, Byzantine-resilient FL framework for a bounded gray-box setting in which adversaries may observe global model trajectories and adapt their updates over time but cannot inspect server-side trust states.
Salam Fraihat, Yousef K. Sanjalawe, Qussai M. Yaseen et al.· Neural computing & applicati...· 0 citations
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduNov 6, 2026
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
