Abstract: In multi-institutional joint security decision-making scenarios,federated learning provides a collaborative decision-ma-king approach that enables data to remain local under privacy-preservation constraints.However,conventional federated-learning-based decision-making algorithms often adopt a traditional binary decision mechanism,which has limited capability in handling uncertainty.Three-way decision is an effective method for addressing uncertain problems,as it enables more prudent deferred judgments for uncertain decision objects.Based on three-way decision theory,this paper proposes a federated three-way security decision-making framework,termed FL-3WSD.The proposed framework introduces the three-way decision mechanism into three levels:local rule generation,multi-source rule fusion,and target object monitoring,including local three-way rule generation on the participant side,three-way multi-source rule fusion on the central cloud server side,and three-way monitoring for target objects.In this framework,the central cloud server first distributes the training model and related parameters to each participant.Then,each participant employs data clustering and sequential three-way decision methods to extract decision rules and uploads them to the central cloud server.Finally,the central cloud server aggregates and fuzzily fuses the decision rules,and further incorporates the three-way decision mechanism to generate a global three-way security decision table,which is ultimately used for the joint judgment of target objects.Compared with classical federated decision-making methods based on binary decision mechanisms,the experimental results show that the FL-3WSD framework achieves clear advantages in decision accuracy and misclassification risk control,demonstrating promising scientific value and application prospects.

Key words: Three-way decision, Federated learning, Privacy preservation, Fuzzy equivalence class, Uncertainty