Many recent breakthroughs in deep learning were achieved by training increasingly larger models on massive datasets. However, training such models can be prohibitively expensive. For instance, the cluster used to train GPT-3 costs over $250 million. As a result, most researchers cannot afford to train state of the art models and contribute to their development. Hypothetically, a researcher could crowdsource the training of large neural networks with thousands of regular PCs provided by volunteers. The raw computing power of a hundred thousand $2500 desktops dwarfs that of a $250M server pod, but one cannot utilize that power efficiently with conventional distributed training methods. In this work, we propose Learning@home: a novel neural network training paradigm designed to handle large amounts of poorly connected participants. We analyze the performance, reliability, and architectural constraints of this paradigm and compare it against existing distributed training techniques.

I was expecting a billion trillion scale evaluation and surprising number in the end because that was the whole motivation. It is very disappointing to see this kind of poor evaluations in a paper with such a good start.

This paper focuses on the use of "citizen science" to train large neural networks. An algorithm is proposed that is fault-tolerant to missing/slow/unreliable nodes, and some preliminary experiments are carried out to demonstrate its utility. The reviewers initially suggested that the experiments were limited, but after rebuttal were convinced that the paper is worth publishing in its current form.

Many recent breakthroughs in deep learning were achieved by training increasingly larger models on massive datasets. However, training such models can be prohibitively expensive. For instance, the cluster used to train GPT-3 costs over 250 million. As a result, most researchers cannot afford to train state of the art models and contribute to their development. Hypothetically, a researcher could crowdsource the training of large neural networks with thousands of regular PCs provided by volunteers.

Many recent breakthroughs in deep learning were achieved by training increasingly larger models on massive datasets. However, training such models can be prohibitively expensive. For instance, Megatron Language Model with 8.3B parameters was trained on a GPU cluster worth \$25 million. As a result, most researchers cannot afford to train state of the art models and contribute to their development. Hypothetically, a researcher could crowdsource the training of large neural networks with thousands of regular PCs provided by volunteers. The raw computing power of ten thousand \$2500 desktops dwarfs that of a \$25M server pod, but one cannot utilize that power efficiently with conventional distributed training methods. In this work, we propose Learning@home: a neural network training paradigm designed to handle millions of poorly connected participants. We analyze the performance, reliability, and architectural constraints of this paradigm and compare it against existing distributed training techniques.