  The world needs diverse and unbiased data to train deep learning models.
Currently data comes from a variety of sources that are unmoderated to a large
extent. The outcomes of training neural networks with unverified data yields
biased models with various strains of homophobia, sexism and racism. Another
trend observed in the world of deep learning is the rise of distributed
training. Although cloud companies provide high performance compute for
training models in the form of GPU's connected with a low latency network,
using these services comes at a high cost. We propose Hydra, a system that
seeks to solve both of these problems in a novel manner by proposing a
decentralized distributed framework which utilizes the substantial amount of
idle compute of everyday electronic devices like smartphones and desktop
computers for training and data collection purposes. Hydra couples a
specialized distributed training framework on a network of these low powered
devices with a reward scheme that incentivizes users to provide high quality
data to unleash the compute capability on this training framework. Such a
system has the ability to capture data from a wide variety of diverse sources
which has been an issue in the current scenario of deep learning. Hydra brings
in several new innovations in training on low powered devices including a fault
tolerant version of the All Reduce algorithm. Furthermore we introduce a
reinforcement learning policy to decide the size of training jobs on different
machines on a heterogeneous cluster of devices with varying network latencies
for Synchronous SGD. The novel thing about such a network is the ability of
each machine to shut down and resume training capabilities at any point of time
without restarting the overall training. To enable such an asynchronous
behaviour we propose a communication framework inspired by the Bittorrent
protocol and the Kademlia DHT.
