Abstract:We consider federated learning in tiered communication networks. Our network model consists of a set of silos, each holding a vertical partition of the data. Each silo contains a hub and a set of clients, with the silo’s vertical data shard partitioned horizontally across its clients. We propose Tiered Decentralized Coordinate Descent (TDCD), a communication-efficient decentralized training algorithm for such two-tiered networks. The clients in each silo perform multiple local gradient steps before sharing upd… Show more
“…This paper presents a federated learning model that utilizes seven indicators, such as the balance of green credit business and transaction information data from commercial banks. Financial institutions, such as banks and consumer finance companies, integrate their own accumulated corporate green financial behavior information with external data related to the company to establish an accurate model (Wu et al, 2020;Das et al, 2022). The risk control model aims to solve problems such as white accounts in credit investigation and green enterprise loan difficulties caused by a lack of effective data, thereby effectively improving the credit risk control level of banks (Sattler et al, 2021;Zhang et al, 2021).…”
Section: Application Practice Of Federated Learning In Bank Credit Ri...mentioning
The green credit policy has significantly influenced the growth of green industries in China. This study evaluates its impact on reducing bank credit risk using data from 26 Chinese banks from 2015 to 2021. The authors discovered that the policy's primary effect is linked to banks' financial leverage. Notably, green credit's influence on insolvency risk is most evident in leverage risk. However, despite governmental support for green credit collaboration, prevalent information gaps between banks and green enterprises lead to misjudgments and subsequent credit losses. To address the balance between credit risk mitigation and privacy, the authors investigated vertical joint learning for a precise risk control model grounded in commercial banks' practices. Experiments revealed that this joint model outperforms the sole “bank internal model” in presenting green credit data, underscoring the potential of machine learning to refine green credit systems and bolster banks' credit risk management.
“…This paper presents a federated learning model that utilizes seven indicators, such as the balance of green credit business and transaction information data from commercial banks. Financial institutions, such as banks and consumer finance companies, integrate their own accumulated corporate green financial behavior information with external data related to the company to establish an accurate model (Wu et al, 2020;Das et al, 2022). The risk control model aims to solve problems such as white accounts in credit investigation and green enterprise loan difficulties caused by a lack of effective data, thereby effectively improving the credit risk control level of banks (Sattler et al, 2021;Zhang et al, 2021).…”
Section: Application Practice Of Federated Learning In Bank Credit Ri...mentioning
The green credit policy has significantly influenced the growth of green industries in China. This study evaluates its impact on reducing bank credit risk using data from 26 Chinese banks from 2015 to 2021. The authors discovered that the policy's primary effect is linked to banks' financial leverage. Notably, green credit's influence on insolvency risk is most evident in leverage risk. However, despite governmental support for green credit collaboration, prevalent information gaps between banks and green enterprises lead to misjudgments and subsequent credit losses. To address the balance between credit risk mitigation and privacy, the authors investigated vertical joint learning for a precise risk control model grounded in commercial banks' practices. Experiments revealed that this joint model outperforms the sole “bank internal model” in presenting green credit data, underscoring the potential of machine learning to refine green credit systems and bolster banks' credit risk management.
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