  Motivated by the explosive computing capabilities at end user equipments, as
well as the growing privacy concerns over sharing sensitive raw data, a new
machine learning paradigm, named federated learning (FL) has emerged. By
training models locally at each client and aggregating learning models at a
central server, FL has the capability to avoid sharing data directly, thereby
reducing privacy leakage. However, the traditional FL framework heavily relies
on a single central server and may fall apart if such a server behaves
maliciously. To address this single point of failure issue, this work
investigates a blockchain assisted decentralized FL (BLADE-FL) framework, which
can well prevent the malicious clients from poisoning the learning process, and
further provides a self-motivated and reliable learning environment for
clients. In detail, the model aggregation process is fully decentralized and
the tasks of training for FL and mining for blockchain are integrated into each
participant. In addition, we investigate the unique issues in this framework
and provide analytical and experimental results to shed light on possible
solutions.
