  Federated learning (FL) is identified as a crucial enabler for large-scale
distributed machine learning (ML) without the need for local raw dataset
sharing, substantially reducing privacy concerns and alleviating the isolated
data problem. In reality, the prosperity of FL is largely due to a centralized
framework called FedAvg, in which workers are in charge of model training and
servers are in control of model aggregation. However, FedAvg's centralized
worker-server architecture has raised new concerns, be it the low scalability
of the cluster, the risk of data leakage, and the failure or even defection of
the central server. To overcome these problems, we propose Decentralized
Federated Trusted Averaging (DeFTA), a decentralized FL framework that serves
as a plug-and-play replacement for FedAvg, instantly bringing better security,
scalability, and fault-tolerance to the federated learning process after
installation. In principle, it fundamentally resolves the above-mentioned
issues from an architectural perspective without compromises or tradeoffs,
primarily consisting of a new model aggregating formula with theoretical
performance analysis, and a decentralized trust system (DTS) to greatly improve
system robustness. Note that since DeFTA is an alternative to FedAvg at the
framework level, \textit{prevalent algorithms published for FedAvg can be also
utilized in DeFTA with ease}. Extensive experiments on six datasets and six
basic models suggest that DeFTA not only has comparable performance with FedAvg
in a more realistic setting, but also achieves great resilience even when 66%
of workers are malicious. Furthermore, we also present an asynchronous variant
of DeFTA to endow it with more powerful usability.
