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