The reported d=2 random-walk universality result is unsupported: the full text is a quantum federated learning survey that never mentions random walks, tail probabilities, or lambda_ext.
Management of Resource at the Network Edge for Federated Learning
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Federated learning has been explored as a promising solution for training at the edge, where end devices collaborate to train models without sharing data with other entities. Since the execution of these learning models occurs at the edge, where resources are limited, new solutions must be developed. In this paper, we describe the recent work on resource management at the edge, and explore the challenges and future directions to allow the execution of federated learning at the edge. Some of the problems of this management, such as discovery of resources, deployment, load balancing, migration, and energy efficiency will be discussed in the paper.
citation-role summary
background 1
citation-polarity summary
fields
cond-mat.stat-mech 1years
2025 1verdicts
REJECT 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Universal Fluctuations in the Tail Probability for d=2 Random Walks in Space-Time Random Environments
The reported d=2 random-walk universality result is unsupported: the full text is a quantum federated learning survey that never mentions random walks, tail probabilities, or lambda_ext.