Distributed Machine Learning through Transceiver Competitive Connectivity of Remote Computing Systems

An architecture is built for sequential data transmission among individual machines participating in a decentralized distributed machine learning of a convolutional neural network for image classification. Simultaneously running multiple replicas of a machine learning task using TCP communication protocol has been successfully implemented. A unification of the final result of each epoch until the end of the training session is performed on each of the participating machines in the training process. A working image classification model is obtained on all machines involved in the process. The results from the experiments performed herein show a 63% improvement in training speed for the fastest configuration with only a 3% drop in accuracy compared to the most accurate one. The study therefore confirms that the use of distributed learning systems can have significant benefits for the AI researchers and practitioners.