Federated Learning (FL) is a distributed machine learning (ML) paradigm,
aiming to train a global model by exploiting the decentralized data across
millions of edge devices. Compared with centralized learning, FL preserves the
clients' privacy by refraining from explicitly downloading their data. However,
given the geo-distributed edge devices (e.g., mobile, car, train, or subway)
with highly dynamic networks in the wild, aggregating all the model updates
from those participating devices will result in inevitable long-tail delays in
FL. This will significantly degrade the efficiency of the training process. To
resolve the high system heterogeneity in time-sensitive FL scenarios, we
propose a novel FL framework, DynamicFL, by considering the communication
dynamics and data quality across massive edge devices with a specially designed
client manipulation strategy. \ours actively selects clients for model updating
based on the network prediction from its dynamic network conditions and the
quality of its training data. Additionally, our long-term greedy strategy in
client selection tackles the problem of system performance degradation caused
by short-term scheduling in a dynamic network. Lastly, to balance the trade-off
between client performance evaluation and client manipulation granularity, we
dynamically adjust the length of the observation window in the training process
to optimize the long-term system efficiency. Compared with the state-of-the-art
client selection scheme in FL, \ours can achieve a better model accuracy while
consuming only 18.9\% -- 84.0\% of the wall-clock time. Our component-wise and
sensitivity studies further demonstrate the robustness of \ours under various
real-life scenarios.

Federated Learning aims to train a global model by utilizing decentralized data, but the highly dynamic networks of edge devices can cause delays and degrade the efficiency of the training process. To address this, DynamicFL is proposed as a novel framework that considers communication dynamics, data quality, and client selection strategies to improve system performance and achieve better model accuracy.

DynamicFL：平衡通信动态和客户端操作的联邦学习

DynamicFL: Balancing Communication Dynamics and Client Manipulation for  Federated Learning

Self-evolving networks (SENs) are emerging technologies that dynamically and
autonomously adapt and optimize their performance and behaviour based on
changing conditions and evolving requirements. With the advent of
fifth-generation (5G) wireless technologies and the resurgence of machine
learning, SENs are expected to become a critical component of future wireless
networks. In particular, integrated vertical heterogeneous network (VHetNet)
architectures, which enable dynamic, three-dimensional (3D), and agile
topologies, are likely to form a key foundation for SENs. However, the
distributed multi-level computational and communication structure and the fully
dynamic nature of self-evolving integrated VHetNets (SEI-VHetNets) necessitate
the deployment of an enhanced distributed learning and computing mechanism to
enable full integration and coordination. To address this need, we propose a
novel learning technique, multi-tier hierarchical federated learning (MT-HFL),
based on hierarchical federated learning (HFL) that enables full integration
and coordination across vertical tiers. Through MT-HFL, SEI-VHetNets can learn
and adapt to dynamic network conditions, optimize resource allocation, and
enhance user experience in a real-time, scalable, and accurate manner while
preserving user privacy. This paper presents the key characteristics and
challenges of SEI-VHetNets and discusses how MT-HFL addresses them. We also
discuss potential use cases and present a case study demonstrating the
advantages of MT-HFL over conventional terrestrial HFL approaches.

本文介绍了自演进集成的竖直异构网络 (SEI-VHetNets) 的特征和挑战，以及多层分层联邦学习 (MT-HFL) 如何解决它们，MT-HFL 允许 SEI-VHetNets 实时、可扩展和准确地学习和适应动态网络条件，优化资源分配，提高用户体验，同时保护用户隐私。