22nd EANN 2021, 25 - 27 June 2021, Greece

Incentivizing Participation to Distributed Neural Network Training

Spyridon Nikolaidis, Ioannis Refanidis


  During the last years a vast number of online sensors continuously generate da-ta that can be utilized to create novel deep learning applications. Training very large models requires enormous processing power; thus, the evident way to fol-low is to lease the power of a corporate data center. But the diffusion of Artifi-cial Intelligence to an always increasing number of human activities, constantly attracts new researchers who wish to train and test their models. Our work on LEARNAE is a proposal for a purely distributed neural network training, based on a peer-to-peer and permissionless architecture. LEARNAE allows individual researchers to join forces, in order to collaboratively train a model. The process utilizes modern Distributed Ledger Technology and it is fully democratized, prioritizing decentralization, fault tolerance and privacy. In this paper we add another piece to the puzzle: A method for incentivizing peers to participate to the training swarm, even if they don’t have any interest in the produced neural network. This is achieved by embedding a reward subsystem to LEARNAE; thus, peers who contribute to teamwork can receive a proportional digital pay-ment.  

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