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The Internet and the Web are being increasingly used in proactive social careto provide people, especially the vulnerable, with a better life and services,and their derived social services generate enormous data. However, the strictprotection of privacy makes user's data become an isolated island and limitsthe predictive performance of standalone clients. To enable effective proactivesocial care and knowledge sharing within intelligent agents, this paperdevelops a knowledge transferring framework via model aggregation. Under thisframework, distributed clients perform on-device training, and a third-partyserver integrates multiple clients' models and redistributes to clients forknowledge transferring among users. To improve the generalizability of theknowledge sharing, we further propose a novel model aggregation algorithm,namely the average difference descent aggregation (AvgDiffAgg for short). Inparticular, to evaluate the effectiveness of the learning algorithm, we use acase study on the early detection and prevention of suicidal ideation, and theexperiment results on four datasets derived from social communities demonstratethe effectiveness of the proposed learning method.

Knowledge Transferring via Model Aggregation for Online Social Care