arXiv AI June 11, 2026 By Masoume Gholizade, Fabrizio Ruffini, Pietro Ducange, Francesco Marcelloni

Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data

arXiv:2606. 11272v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative and privacy-preserving model training across distributed clients, but most existing FL systems implicitly assume data stationarity.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 10

From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning

arXiv:2606. 10595v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a promising solution for data hunger in centralized learning.

By Huong Nguyen, Micka\"el Bettinelli, Amirhossein Ghaffari, Alexandre Benoit, Hong-Tri Nguyen, Susanna Pirttikangas, Lauri Lov\'en

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arXiv AI

Jun 8

Towards Efficient and Exact Forgetting Services in Pre-Trained-Model-based Continual Learning

arXiv:2505. 12239v2 Announce Type: replace-cross Abstract: In Continual Learning (CL), using a Pre-Trained Model (PTM) as the feature extractor has become a popular practice.

By Yajiang Huang, Jianheng Tang, Kejia Fan, Huiping Zhuang, Anfeng Liu, Tian Wang, Yunhuai Liu, Mianxiong Dong, Houbing Herbert Song

benchmarks safety

arXiv Machine Learning

Jun 26

Quantization in Federated Learning: Methods, Challenges and Future Directions

arXiv:2606. 26822v1 Announce Type: new Abstract: Federated Learning (FL) has become a foundational paradigm for privacy-preserving distributed intelligence, yet its scalability remains fundamentally constrained by communication bottlenecks, device heterogeneity, and the challenges of training under statistically non-IID data.

By Farwa Ikram, Dipanwita Thakur, Antonella Guzzo, Giancarlo Fortino

efficiency safety

arXiv AI

Jun 16

Beyond Weights and Gradients: A Taxonomy of Federated Learning Messages

arXiv:2606. 16891v1 Announce Type: cross Abstract: Federated Learning is rapidly evolving beyond the exchange of traditional model weights and gradients, yet existing definitions fail to capture the full scope of modern payloads like synthetic data and federated analytics.

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Hugging Face Trending Papers

Jun 1

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ $\varepsilon$-aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets.

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arXiv Machine Learning

Jun 2

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

arXiv:2606. 02563v1 Announce Type: new Abstract: Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity.

By Farhin Farhad Riya, Olivera Kotevska, Jinyuan Stella Sun

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