{"total":26,"rows":[{"id":"2106.15482","title":"Personalized Federated Learning with Gaussian Processes","created":"2021-06-29","cited_by_count":76,"area":null},{"id":"2303.06937","title":"TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation","created":"2023-03-13","cited_by_count":65,"area":null},{"id":"2312.16475","title":"Federated Continual Learning via Knowledge Fusion: A Survey","created":"2023-12-27","cited_by_count":53,"area":null},{"id":"2107.10996","title":"Communication Efficiency in Federated Learning: Achievements and Challenges","created":"2021-07-23","cited_by_count":40,"area":null},{"id":"2206.03611","title":"FedPop: A Bayesian Approach for Personalised Federated Learning","created":"2022-06-07","cited_by_count":35,"area":null},{"id":"2106.00797","title":"QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning","created":"2021-06-01","cited_by_count":26,"area":null},{"id":"2112.13246","title":"Towards Federated Learning on Time-Evolving Heterogeneous Data","created":"2021-12-25","cited_by_count":23,"area":null},{"id":"2403.14101","title":"Text-Enhanced Data-free Approach for Federated Class-Incremental Learning","created":"2024-03-21","cited_by_count":16,"area":null},{"id":"2111.01721","title":"Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees","created":"2021-11-02","cited_by_count":13,"area":null},{"id":"2106.06300","title":"DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs","created":"2021-06-11","cited_by_count":11,"area":null},{"id":"2202.11154","title":"Parallel MCMC Without Embarrassing Failures","created":"2022-02-22","cited_by_count":11,"area":null},{"id":"2111.14008","title":"Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling","created":"2021-11-28","cited_by_count":8,"area":null},{"id":"2107.09627","title":"Precision-Weighted Federated Learning","created":"2021-07-20","cited_by_count":6,"area":null},{"id":"2401.08351","title":"Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach","created":"2024-01-16","cited_by_count":5,"area":null},{"id":"2211.02476","title":"Sparse Gaussian Process Hyperparameters: Optimize or Integrate?","created":"2022-11-04","cited_by_count":4,"area":null},{"id":"2212.05492","title":"Client Selection for Federated Bayesian Learning","created":"2022-12-11","cited_by_count":4,"area":null},{"id":"2408.06069","title":"Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations","created":"2024-08-12","cited_by_count":4,"area":null},{"id":"2501.17325","title":"Connecting Federated ADMM to Bayes","created":"2025-01-28","cited_by_count":4,"area":null},{"id":"2209.11595","title":"Differentially private partitioned variational inference","created":"2022-09-23","cited_by_count":2,"area":null},{"id":"2110.13515","title":"Modular Gaussian Processes for Transfer Learning","created":"2021-10-26","cited_by_count":1,"area":null},{"id":"2309.02606","title":"Distributed Variational Inference for Online Supervised Learning","created":"2023-09-05","cited_by_count":1,"area":null},{"id":"2406.02177","title":"One-Shot Federated Learning with Bayesian Pseudocoresets","created":"2024-06-04","cited_by_count":1,"area":null},{"id":"2502.00846","title":"Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework","created":"2025-02-02","cited_by_count":1,"area":null},{"id":"2112.09794","title":"Robust Distributed Bayesian Learning with Stragglers via Consensus Monte Carlo","created":"2021-12-17","cited_by_count":0,"area":null},{"id":"2406.01801","title":"Fearless Stochasticity in Expectation Propagation","created":"2024-06-03","cited_by_count":0,"area":null},{"id":"2609.21762","title":"Neural composite likelihood estimation: simulation based inference for time series","created":"2026-09-18","cited_by_count":0,"area":null}],"next_cursor":null}