This paper develops efficient federated learning and unlearning methods in Bayesian models.
problem Managing epistemic uncertainty and legal right to be forgotten in decentralized networks.
method Develops federated variational inference solutions based on decentralized local free energy minimization.
result Demonstrates efficient unlearning mechanisms in federated learning and unlearning.
New federated learning method for structured models.
problem Limited federated learning for structured probabilistic models.
method Structured variational inference adapted for federated learning.
result Effective algorithms compared to hierarchical Bayesian models.
A new algorithm improves Bayesian federated learning by reducing communication overhead.
problem Bayesian federated learning constraints, including privacy, data ownership, and communication overhead.
method Proposes Quantised Langevin Stochastic Dynamics (QLSD) for Bayesian federated learning, using gradient compression and variance reduction techniques.
result Non-asymptotic and asymptotic convergence guarantees for QLSD and its improved versions.
Paper improves Bayesian inference in federated learning with new algorithm VR-FALD*.
problem Bayesian inference in federated learning with communication bottlenecks and statistical heterogeneity.
method Federated Averaging Langevin Dynamics (FALD) and VR-FALD*.
result VR-FALD* corrects client drift due to statistical heterogeneity, improving convergence.
FA-HMC improves Bayesian federated learning with rigorous guarantees.
problem Parameter estimation and uncertainty quantification in non-iid distributed data.
method Federated Averaging stochastic Hamiltonian Monte Carlo (FA-HMC) with convergence guarantees.
result FA-HMC achieves better convergence and communication efficiency than existing methods.
Federated learning for Bayesian clustering of large datasets.
problem Bayesian model-based clustering of large-scale binary and categorical data.
method Federated variational inference with local merge and delete moves in parallel batches, followed by global merge moves.
result Empirical validation shows superior performance compared to existing algorithms.
DSVGD improves federated learning with fewer communication rounds.
problem Federated learning scalability and trustworthiness.
method Distributed Stein Variational Gradient Descent (DSVGD) for non-parametric Bayesian inference.
result DSVGD achieves comparable accuracy and scalability to other methods, with well-calibrated predictions.
Bayesian approach generalizes ADMM for federated learning.
problem Improving federated learning efficiency and accuracy.
method Integrates Bayesian duality with ADMM for optimization.
result New extensions of ADMM for various distributions.
Bayesian Federated Learning improves model reliability in dynamic environments.
problem Uncertainty quantification and robust adaptation in distributed learning.
method Proposes a continual BFL framework using SGLD for sequential updates and continual learning challenges.
result Continual Bayesian updates preserve knowledge and adapt to evolving data.
Bayesian method improves Federated Learning robustness against corrupted updates.
problem Adversarial attacks on Federated Learning models with unknown number of compromised clients.
method Adaptive Bayesian aggregation based on likelihood of clients being honest.
result Consistently achieves state-of-the-art performance across various attack types.
GRAFFL learns Bayesian models without gradients for federated learning.
problem Privacy protection in federated learning during gradient updates.
method Gradient-free approach using summary statistics from SuffiAE.
result Feasibility and usefulness demonstrated in Gaussian mixture model.
We consider the problem of reinforcing federated learning with formal privacy guarantees. We propose to employ Bayesian differential privacy, a relaxation of differential privacy for similarly distributed data, to provide sharper privacy loss bounds. We adapt the Bayesian privacy accounting method to the federated sett…
Unified Bayesian framework for clustered federated learning improves model performance.
problem Handling non-IID client data in federated learning.
method A unified Bayesian framework for clustered federated learning that associates clients to clusters and proposes practical algorithms for data associations.
result The proposed framework increases model performance by circumventing the need for unique client-cluster associations.
Proposes a Bayesian federated learning method for diverse tasks.
problem Current federated learning approaches focus on homogeneous tasks, ignoring task diversity.
method Integrates multi-task learning with MOGP at the local level and federated learning at the global level.
result Demonstrates superior predictive performance and uncertainty calibration on diverse tasks.
A new method for privacy-preserving Bayesian learning in federated learning.
problem Privacy-preserving learning of models from distributed sensitive data.
method Differentially private partitioned variational inference (DPVI) for federated learning.
result First general framework for federated Bayesian learning with differential privacy.
Federated learning for Bayesian network structure learning across distributed data.
problem Learning Bayesian network structure from horizontally partitioned data across different parties.
method Distributed structure learning using continuous optimization (ADMM).
result Improved performance compared to other methods, especially with many clients and limited data.
In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited. We develop a Bayesian nonparametric framework for federated learning with neural networks. Each data server is assumed to provide local neural network weights, which are modeled th…
This work analyzes aggregation strategies for Bayesian deep learning models in federated learning.
problem Improper aggregation of Bayesian deep learning models in federated learning leads to sub-optimal performance.
method Six aggregation strategies for Bayesian deep learning models are analyzed using CIFAR-10 dataset and a fully variational ResNet-20 architecture.
result Aggregation strategy is a key hyperparameter affecting accuracy, calibration, uncertainty quantification, training stability, and client compute requirements.
DPMM-CFL clusters clients for federated learning without fixed K, improving performance.
problem Improving federated learning performance under non-IID client heterogeneity.
method DPMM-CFL uses a Dirichlet Process Mixture Model to infer both cluster number and client assignments.
result DPMM-CFL optimizes per-cluster federated objectives and jointly infers cluster number and assignments.
FSL-BDP models time-to-default without centralizing data, improving privacy mechanisms in federated settings.
problem Traditional credit risk models ignore default timing and violate data-protection rules.
method Federated Survival Learning with Bayesian Differential Privacy (FSL-BDP).
result FSL-BDP improves privacy mechanisms in federated settings, outperforming classical DP in most clients.
A novel method for learning Bayesian network structures from decentralized data, balancing privacy and efficiency.
problem Privacy and communication costs in learning Bayesian network structures from decentralized data.
method Fed-Sparse-BNSL, combining differential privacy with greedy updates targeting only a few relevant edges per participant.
result Achieves utility close to non-private baselines while offering stronger privacy and communication efficiency.
FedBE aggregates local models into a robust global model via Bayesian inference.
problem Challenges in aggregating non-i.i.d. local models into a global model in federated learning.
method FedBE uses Bayesian inference to sample and combine higher-quality global models from local models.
result FedBE leads to more robust aggregation of local models into a global model, especially when data is non-i.i.d.
FedLog reduces communication in federated learning by sharing data summaries.
problem Significant communication overhead in federated learning with large model parameters.
method Shares minimal sufficient statistics via Bayesian inference and differential privacy.
result High learning accuracy with low communication overhead.
In many real-world applications of machine learning, data are distributed across many clients and cannot leave the devices they are stored on. Furthermore, each client's data, computational resources and communication constraints may be very different. This setting is known as federated learning, in which privacy is a …
Paper tackles scalable VFL with data augmentation and amortized inference.
problem Collaborative model estimation across multiple clients with distinct covariates.
method Data augmentation, amortized variational approximation, factorized likelihoods.
result Scalable Bayesian VFL framework for various models.
We address challenges in collaborative black-box optimization through three frameworks.
problem Challenges in distributed experimentation, heterogeneity, and privacy in black-box optimization.
method Three unifying frameworks: global, local, and predictive.
result Shift from descriptive/predictive to prescriptive federated learning in black-box optimization.
A novel hierarchical Bayesian approach to Federated Learning reduces data exposure and improves convergence rates.
problem Data privacy and convergence in Federated Learning.
method Hierarchical Bayesian modeling and block-coordinate descent optimization.
result The proposed algorithm converges to an optimal solution with a rate of O ( 1 / t ) O(1/\sqrt{t}) O ( 1/ t ) and guarantees vanishing generalization error. Bayesian VFLMSP improves multimodal survival prediction with privacy.
problem Privacy and reliability in multimodal time-to-event prediction.
method Bayesian Vertical Federated Learning (VFL) with differential privacy.
result Consistent improvements in C-index compared to existing methods.
Improved Bayesian FL method calibrates predictions for federated learning.
problem Overconfident predictions in Bayesian FL methods for federated learning.
method β-Predictive Bayes algorithm interpolates between mixture and product of local predictive posteriors, tuning parameter β for better calibration.
result Demonstrated superior calibration compared to other baselines, even with increased data heterogeneity.
In federated learning, a central server coordinates the training of a single model on a massively distributed network of devices. This setting can be naturally extended to a multi-task learning framework, to handle real-world federated datasets that typically show strong statistical heterogeneity among devices. Despite…
Bayesian Federated Inference improves statistical model estimation from multicenter data.
problem Combining data from different medical centers is challenging due to regulatory and logistic issues.
method Bayesian Federated Inference (BFI) framework for multicenter data.
result BFI framework infers additional features of the posterior parameter distribution, capturing more information than Federated Learning.
Paper proposes incentives for federated learning to ensure truthful contributions.
problem Ensuring truthful contributions from decentralized users in federated learning.
method Introduces a scoring rule based framework to incentivize truthful reporting of local hypotheses at a Bayesian Nash Equilibrium.
result Proposed solution verified using MNIST and CIFAR-10 datasets, showing decreasing scores for low-quality hypotheses.
Bayesian approach reduces FL communication cost by one-shot.
problem High communication cost in optimization-based FL for high-dimensional models.
method Bayesian pseudocoresets and function-space inference for one-shot FL.
result Achieves prediction performance competitive to state-of-the-art with up to 2 orders of magnitude reduction in communication cost.
Bayesian mixture models inference in federated learning
problem Inference of Bayesian mixture models in federated learning
method Consensus Monte Carlo approach
result Recovery of small clusters with greater accuracy than standard MCMC
DaringFed incentivizes clients in OFL with dynamic rewards under TII.
problem Designing incentives for OFL clients under dynamic, incomplete information.
method Formulated as a dynamic signaling and pricing allocation problem in a Bayesian persuasion game.
result Optimal design of DaringFed improves accuracy and convergence speed by 16.99%.
Novel strategy for federated learning with privacy-preserving predictors and nonvacuous generalization bounds.
problem Privacy-preserving federated learning with nonvacuous generalization bounds.
method Randomized predictors, PAC-Bayesian generalization bound, synchronous and heterogeneous/homogenous cases.
result Achieves comparable predictive performance to batch approach while preserving privacy.
Efficient Bayesian FL method improves predictive accuracy and uncertainty estimates.
problem Federated Learning with model and predictive uncertainty and personalization.
method Second-order optimization approach for Bayesian FL.
result Improved predictive accuracies and uncertainty estimates.
In clinical research, the lack of events of interest often necessitates imbalanced learning. One approach to resolve this obstacle is data integration or sharing, but due to privacy concerns neither is practical. Therefore, there is an increasing demand for a platform on which an analysis can be performed in a federate…
FMP sampling improves model calibration without sharing data.
problem Difficulties in specifying priors for modern neural networks.
method FMP sampling replaces prior and likelihood with a predictive distribution, running the sampler centrally.
result FMP sampling significantly improves calibration over baseline methods.
Group personalization improves FL performance in heterogeneous client data.
problem Mitigating client drift in federated learning with heterogeneous data.
method Fine-tuning a global FL model over homogeneous groups of clients, then personalizing each group's model.
result The proposed method achieves superior personalization performance compared to other FL approaches.
A new method speeds up DPMM inference for federated learning.
problem Slow inference for large datasets in DPMMs.
method Distributed collapsed Gibbs sampler (DisCGS) for DPMMs.
result Significant reduction in execution time (200x faster) for large datasets.
FedGVI improves FL robustness to model misspecification.
problem Limited robustness in FL approaches to model misspecification.
method Probabilistic Federated Learning framework that generalizes previous methods.
result FedGVI provides robust and calibrated predictions under model misspecification.
New algorithm reduces dimensionality in federated learning.
problem Estimating central dimension reduction subspace and variable selection in federated learning.
method Federated sparse sliced inverse regression, convex optimization, linearized alternating direction method of multipliers.
result Upper bound of statistical error rate established under heterogeneous setting.
EP-GFlowNets parallelize GFlowNet training for large-scale Bayesian inference.
problem Prohibitive repeated evaluations of unnormalized distributions for large-scale posterior sampling.
method Divide-and-conquer approach with server learning from local models.
result EP-GFlowNets enable efficient parallel and federated Bayesian inference.
Proposes FedPop for personalised federated learning with uncertainty quantification.
problem Uncertainty quantification and client drift in personalised federated learning.
method FedPop recasts FL into population modeling with Markov chain Monte Carlo methods.
result Non-asymptotic convergence guarantees for uncertainty quantification.
Federated learning framework with uncertainty estimation.
problem Uncertainty estimation in federated learning models.
method Infers and distills posterior predictive distributions from clients to server.
result Improved accuracy and reliability in predictions and tasks like OOD detection.
Develops an online federated learning framework for classification.
problem Handling streaming data from multiple clients while ensuring data privacy and efficiency.
method Leverages generalized distance-weighted discriminant technique and Majorization-Minimization principle.
result Achieves high classification accuracy, significant computational efficiency, and data security enhancements.
Fed-ensemble improves FL by averaging predictions from multiple models.
problem Improving generalization in federated learning.
method Random permutations to update K models, averaging predictions.
result Predictions from all K models have the same predictive posterior distribution.