Gedistribueerd Leren, Optimalisatie en Signaalschatting met Toepassingen in Multi-agentsystemen en Communicatienetwerken

In today's world, a vast number of devices, such as cell phones, personal computers, robots, wearable gadgets, etc., are contributing to our daily lives at an ever-increasing pace. These devices are mostly equipped with multiple sensors, capturing data in different modalities. For instance, microphones capture audio signals, cameras record images and videos, and biosensors monitor biomedical information. These devices also have memory and computational capacities, enabling efficient learning and signal processing, resulting in, e.g., accurate object recognition in images and powerful denoising techniques for better sound quality in calls. Although these processing capabilities enable each device to capture and analyze data efficiently, cooperation between devices can result in higher and more robust performance. Through proper distributed learning and signal estimation algorithms, sharing multi-modal data between devices guarantees better generalization in learning tasks—specifically for data-hungry learning tasks—and ensures better estimation of signal statistics, thereby improving signal processing quality. Furthermore, through efficient distributed optimization algorithms, sharing computational power enables each device to perform more demanding tasks in a more robust manner. While cooperation offers each agent (device) various benefits in multi-agent systems, it brings multiple challenges in distributed learning, optimization, and signal estimation that need to be addressed to achieve higher performance compared to single-agent scenarios. Multi-agent systems need to accommodate dynamic system configurations and adapt quickly to new conditions, as agents may freely enter or leave the system, each with dynamic demands and constraints. Cooperation also calls for devising frameworks that can address heterogeneity in data distribution and in the communication, memory, and computational capacities of agents. Communication between agents may also be unreliable, with random delays and limited resources, such as bandwidth; hence, asynchronicity and resource allocation (RA) need to be taken into account. Mutual interactions between agents, in the sense that each agent's performance is affected by other agents' actions and decisions, are necessary to be considered for higher performance. Privacy concerns and addressing nonidealities, e.g., nonlinearities, are also among the imperative challenges in multi-agent systems. The objective of this thesis is to address the aforementioned challenges in distributed learning, optimization, and signal estimation by developing relevant methods and algorithms. Mathematical tools and methods such as zeroth-order optimization, biased stochastic gradient descent, variational inequalities, the Moreau envelope, and distributed adaptive node-specific signal estimation are considered to devise various distributed algorithms with convergence and adaptation guarantees for multiple applications across the chapters of the thesis. In the first three chapters, the developed distributed learning algorithms are applied to deep learning-based RA in wireless communication networks, multi-task learning in generic multi-agent systems, and federated learning. A superlinear convergent optimization algorithm is developed in the next chapter for regularized nonconvex finite sum minimization, a problem at the core of many machine learning and signal processing applications. The developed optimization algorithm has favorable global convergence guarantees and can handle a broader class of nonconvex functions, specifically those that are non-Lipschitz differentiable. In the last chapter, distributed signal estimation is investigated to develop a cooperative uplink channel estimation algorithm in cell-free massive multi-input-multi-output communication networks.