Decentralized Learning over Wireless Networks with Imperfect and Constrained Communication : To broadcast, or not to broadcast, that is the question!

Abstract: The ever-expanding volume of data generated by network devices such as smartphones, personal computers, and sensors has significantly contributed to the remarkable advancements in artificial intelligence (AI) and machine learning (ML) algorithms. However, effectively processing and learning from this extensive data usually requires substantial computational capabilities centralized in a server. Moreover, concerns regarding data privacy arise when collecting training data from distributed network devices. To address these challenges, collaborative ML with decentralized data has emerged as a promising solution for large-scale machine learning across distributed devices, driven by the parallel computing and learning trends. Collaborative and distributed ML can be broadly classified into two types: server-based and fully decentralized, based on whether the model aggregation is coordinated by a parameter server or performed in a decentralized manner through peer-to-peer communication. In cases where communication between devices occurs over wireless links, which are inherently imperfect, unreliable, and resource-constrained, how can we design communication protocols to achieve the best learning performance? This thesis investigates decentralized learning using decentralized stochastic gradient descent, an established algorithm for decentralized ML, in a novel setting with imperfect and constrained communication. "Imperfect" implies that communication can fail and "constrained" implies that communication resources are limited. The communication across a link between two devices is modeled as a binary event with either success or failure, depending on if multiple neighbouring devices are transmitting information. To compensate for communication failures, every communication round can have multiple communication slots, which are limited and must be carefully allocated over the learning process. The quality of communication is quantified by introducing normalized throughput, describing the ratio of successful links in a communication round. To decide when devices should broadcast, both random and deterministic medium access policies have been developed with the goal of maximizing throughput, which has shown very efficient learning performance. Finally, two schemes for allocating communication slots over communication rounds have been defined and simulated: Delayed-Allocation and the Periodic-Allocation schemes, showing that it is better to allocate slots late rather than early, and neither too frequently nor infrequently which can depend on several factors and requires further study