Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

To satisfy the expected plethora of computation-heavy applications, federated edge learning
(FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency …

By enabling multiple agents to cooperatively solve a global optimization problem in the
absence of a central coordinator, decentralized stochastic optimization is gaining increasing …

Federated learning (FL) over mobile devices has fostered numerous intriguing
applications/services, many of which are delay-sensitive. In this paper, we propose a service …

Performative prediction is a recently proposed framework where predictions guide decision-
making and hence influence future data distributions. Such performative phenomena are …

This paper considers improving wireless communication and computation efficiency in
federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge …

Distributed learning is an important task in emerging applications such as localization and
navigation, Internet-of-Things, and autonomous vehicles. This paper establishes a …

Distributed learning is crucial for many applications such as localization and tracking,
autonomy, and crowd sensing. This paper investigates communication-efficient distributed …

Due to the iterative information exchange between agents, decentralized multi-agent
optimization algorithms often incur large communication overhead, which is not affordable in …

The decentralized Federated Learning (FL) setting avoids the role of a potentially unreliable
or untrustworthy central host by utilizing groups of clients to collaboratively train a model via …