Study on Blockchain Based Federated Distillation Data Sharing Model
The privacy of raw data makes it difficult to be directly shared among multiple participants.The issue of data security sharing and privacy-preserving has become a hot research topic.To solve this problem,this paper proposes a blockchain-based be-derated distillation data sharing model(BFDS).It utilizes blockchain to form a collaborative teacher network with multiple partici-pants.Through distilled output exchange,the knowledge from complex teacher networks is transferred and used to train light-weight models.A novel multi-weight node trust evaluation algorithm is proposed that uses smart contracts to generate traceable global soft labels.It can reduce the negative impact caused by quality differences among participants.Experimental results show that BFDS can collaborate with multiple parties to share data knowledge reliably,distill training models collaboratively,and re-duce model deployment costs.The proposed algorithm can effectively reduce the negative impact of low-quality nodes and improve the quality and security of global soft labels.
万方数据
维普
