Research Article
Privacy-Preserving Federated Learning Framework with General Aggregation and Multiparty Entity Matching
Input: a central server CS, a set of participants , instance space of samples of each participants, subshares , cyclic group , and its primitive . | Output: federated logistic regression model. | 1: fordo | 2: fordo | 3: computes . | 4: chooses random number and makes public its . | 5: uses others’ to compute and sends it to CS. | 6: fordo | 7: if someone exits then | 8: CS eliminates the value involving information of quitters in . | 9: CS performs the aggregation and decrypts to get . | 10: CS computes . | 11: broadcasts . | 12: Each participant and CS can update weight parameter by computing . | 13: Repeat all until reaching the termination condition. | 14: return built model. |
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