Federated learning (FL) is a machine learning technique that enables multiple decentralized organizations to train a model without exposing local data samples. Instead, during the training, lots of encrypted messages will be exchanged among the participants to aggregate the global model. Due to the message is so important and its requirements of real-time and sequential, it brings some challenges to the transmission. In this session, we will talk about how to address the above challenge with the Apache Pulsar project, and we will go through the details about how popular FL project FATE(https://github.com/FederatedAI/FATE) use Pulsar to do federated training.
Jiahao Chen, software engineer at VMware, specializes in container, network and distributed technology research, and is also actively involved in the construction of open source communities. During his tenure at VMware, he led or participated in the completion of the construction of multiple platform-level projects and is also one of the four maintainers of the open source project Hyperledger-Cello. Familiar with technologies such as virtualization, cloud computing and blockchain.