  The current standalone deep learning framework tends to result in overfitting
and low utility. This problem can be addressed by either a centralized
framework that deploys a central server to train a global model on the joint
data from all parties, or a distributed framework that leverages a parameter
server to aggregate local model updates. Server-based solutions are prone to
the problem of a single-point-of-failure. In this respect, collaborative
learning frameworks, such as federated learning (FL), are more robust. Existing
federated learning frameworks overlook an important aspect of participation:
fairness. All parties are given the same final model without regard to their
contributions. To address these issues, we propose a decentralized Fair and
Privacy-Preserving Deep Learning (FPPDL) framework to incorporate fairness into
federated deep learning models. In particular, we design a local credibility
mutual evaluation mechanism to guarantee fairness, and a three-layer
onion-style encryption scheme to guarantee both accuracy and privacy. Different
from existing FL paradigm, under FPPDL, each participant receives a different
version of the FL model with performance commensurate with his contributions.
Experiments on benchmark datasets demonstrate that FPPDL balances fairness,
privacy and accuracy. It enables federated learning ecosystems to detect and
isolate low-contribution parties, thereby promoting responsible participation.
