Bayesian Variational Federated Learning and Unlearning in Decentralized Networks
Federated Bayesian learning offers a principled framework for the definition of collaborative training algorithms that are able to quantify epistemic uncertainty and to produce trustworthy decisions. Upon the completion of collaborative training, an agent may decide to exercise her legal "right to be forgotten", which calls for her contribution to the jointly trained model to be deleted and discarded. This paper studies federated learning and unlearning in a decentralized network within a Bayesian framework. It specifically develops federated variational inference (VI) solutions based on the decentralized solution of local free energy minimization problems within exponential-family models and on local gossip-driven communication. The proposed protocols are demonstrated to yield efficient unlearning mechanisms.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningVariational InferenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Forget-SVGD: Particle-Based Bayesian Federated Unlearning
Variational particle-based Bayesian learning methods have the advantage of not being limited by the bias affecting more conventional parametric techniques. This paper proposes to leverage the flexibility of non-parametri…
Bayesian InferenceFederated LearningBayesian Federated Model Compression for Communication and Computation Efficiency
In this paper, we investigate Bayesian model compression in federated learning (FL) to construct sparse models that can achieve both communication and computation efficiencies. We propose a decentralized Turbo variationa…
Bayesian InferenceFederated LearningModel CompressionBadFU: Backdoor Federated Learning through Adversarial Machine Unlearning
Federated learning (FL) has been widely adopted as a decentralized training paradigm that enables multiple clients to collaboratively learn a shared model without exposing their local data. As concerns over data privacy …
Federated LearningSky of Unlearning (SoUL): Rewiring Federated Machine Unlearning via Selective Pruning
The Internet of Drones (IoD), where drones collaborate in data collection and analysis, has become essential for applications such as surveillance and environmental monitoring. Federated learning (FL) enables drones to t…
Data PoisoningFederated LearningMachine UnlearningFedShard: Federated Unlearning with Efficiency Fairness and Performance Fairness
To protect clients' right to be forgotten in federated learning, federated unlearning aims to remove the data contribution of leaving clients from the global learned model. While current studies mainly focused on enhanci…
Federated Learning