Federated Learning (FL) is a machine learning approach that enables the
creation of shared models for powerful applications while allowing data to
remain on devices. This approach provides benefits such as improved data
privacy, security, and reduced latency. However, in some systems, direct
communication between clients and servers may not be possible, such as remote
areas without proper communication infrastructure. To overcome this challenge,
a new framework called FedEx (Federated Learning via Model Express Delivery) is
proposed. This framework employs mobile transporters, such as UAVs, to
establish indirect communication channels between the server and clients. These
transporters act as intermediaries and allow for model information exchange.
The use of indirect communication presents new challenges for convergence
analysis and optimization, as the delay introduced by the transporters'
movement creates issues for both global model dissemination and local model
collection. To address this, two algorithms, FedEx-Sync and FedEx-Async, are
proposed for synchronized and asynchronized learning at the transporter level.
Additionally, a bi-level optimization algorithm is proposed to solve the joint
client assignment and route planning problem. Experimental validation using two
public datasets in a simulated network demonstrates consistent results with the
theory, proving the efficacy of FedEx.

提出了一种名为 FedEx 的框架，该框架使用移动运输工具（如无人机）来建立服务器和客户端之间的间接通信通道，实现了联邦学习的远程访问，并提出了两种算法 FedEx-Sync 和 FedEx-Async，以解决运输工具级别上的同步和异步学习，以及双层优化算法，以解决客户分配和路径规划问题。制定实验验证 FedEx 的一致结果证明其有效性。

联邦学习间接通信中的客户端分配和无人机路径规划

Joint Client Assignment and UAV Route Planning for  Indirect-Communication Federated Learning

Tuning hyperparameters is a crucial but arduous part of the machine learning
pipeline. Hyperparameter optimization is even more challenging in federated
learning, where models are learned over a distributed network of heterogeneous
devices; here, the need to keep data on device and perform local training makes
it difficult to efficiently train and evaluate configurations. In this work, we
investigate the problem of federated hyperparameter tuning. We first identify
key challenges and show how standard approaches may be adapted to form
baselines for the federated setting. Then, by making a novel connection to the
neural architecture search technique of weight-sharing, we introduce a new
method, FedEx, to accelerate federated hyperparameter tuning that is applicable
to widely-used federated optimization methods such as FedAvg and recent
variants. Theoretically, we show that a FedEx variant correctly tunes the
on-device learning rate in the setting of online convex optimization across
devices. Empirically, we show that FedEx can outperform natural baselines for
federated hyperparameter tuning by several percentage points on the
Shakespeare, FEMNIST, and CIFAR-10 benchmarks, obtaining higher accuracy using
the same training budget.

本篇论文研究联邦超参数调整的问题，并提出了一种名为 FedEx 的新方法，该方法通过联系神经结构搜索技术和联邦优化方法，加速联邦超参数调整，影响研究者的广泛关注。