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Distributed machine learning (ML) can bring more computational resources tobear than single-machine learning, thus enabling reductions in training time.Distributed learning partitions models and data over many machines, allowingmodel and dataset sizes beyond the available compute power and memory of asingle machine. In practice though, distributed ML is challenging whendistribution is mandatory, rather than chosen by the practitioner. In suchscenarios, data could unavoidably be separated among workers due to limitedmemory capacity per worker or even because of data privacy issues. There,existing distributed methods will utterly fail due to dominant transfer costsacross workers, or do not even apply.  We propose a new approach to distributed fully connected neural networklearning, called independent subnet training (IST), to handle these cases. InIST, the original network is decomposed into a set of narrow subnetworks withthe same depth. These subnetworks are then trained locally before parametersare exchanged to produce new subnets and the training cycle repeats. Such anaturally "model parallel" approach limits memory usage by storing only aportion of network parameters on each device. Additionally, no requirementsexist for sharing data between workers (i.e., subnet training is local andindependent) and communication volume and frequency are reduced by decomposingthe original network into independent subnets. These properties of IST can copewith issues due to distributed data, slow interconnects, or limited devicememory, making IST a suitable approach for cases of mandatory distribution. Weshow experimentally that IST results in training times that are much lower thancommon distributed learning approaches.

Distributed Learning of Deep Neural Networks using Independent Subnet Training