Satellite technologies have advanced drastically in recent years, leading to
a heated interest in launching small satellites into low Earth orbit (LEOs) to
collect massive data such as satellite imagery. Downloading these data to a
ground station (GS) to perform centralized learning to build an AI model is not
practical due to the limited and expensive bandwidth. Federated learning (FL)
offers a potential solution but will incur a very large convergence delay due
to the highly sporadic and irregular connectivity between LEO satellites and
GS. In addition, there are significant security and privacy risks where
eavesdroppers or curious servers/satellites may infer raw data from satellites'
model parameters transmitted over insecure communication channels. To address
these issues, this paper proposes FedSecure, a secure FL approach designed for
LEO constellations, which consists of two novel components: (1) decentralized
key generation that protects satellite data privacy using a functional
encryption scheme, and (2) on-orbit model forwarding and aggregation that
generates a partial global model per orbit to minimize the idle waiting time
for invisible satellites to enter the visible zone of the GS. Our analysis and
results show that FedSecure preserves the privacy of each satellite's data
against eavesdroppers, a curious server, or curious satellites. It is
lightweight with significantly lower communication and computation overheads
than other privacy-preserving FL aggregation approaches. It also reduces
convergence delay drastically from days to only a few hours, yet achieving high
accuracy of up to 85.35% using realistic satellite images.

FedSecure 是一种用于低地球轨道（LEO）星座的安全联邦学习方法，它采用分布式密钥生成和轨道上的模型转发与聚合两个组件，以保护卫星数据的隐私并减少收敛延迟。

基于去中心化密钥生成和轨道上模型聚合的低地轨道星座中安全高效的联邦学习

Secure and Efficient Federated Learning in LEO Constellations using  Decentralized Key Generation and On-Orbit Model Aggregation

We consider an asynchronous hierarchical federated learning (AHFL) setting
with a client-edge-cloud framework. The clients exchange the trained parameters
with their corresponding edge servers, which update the locally aggregated
model. This model is then transmitted to all the clients in the local cluster.
The edge servers communicate to the central cloud server for global model
aggregation. The goal of each client is to converge to the global model, while
maintaining timeliness of the clients, i.e., having optimum training iteration
time. We investigate the convergence criteria for such a system with dense
clusters. Our analysis shows that for a system of $n$ clients with fixed
average timeliness, the convergence in finite time is probabilistically
guaranteed, if the nodes are divided into $O(1)$ number of clusters, that is,
if the system is built as a sparse set of edge servers with dense client bases
each.

文章研究了一种客户端 - 边缘 - 云异步分层联邦学习的设置，通过局部聚合模型的更新并在边缘服务器之间进行交换以收敛于全局模型，最终得到一系列稠密的客户端基地。分析表明，如果将节点分成 O（1）个集群，则对于固定平均时效的 n 个客户端的系统，可以在有限时间内概率上保证收敛。

及时异步分层联邦学习：融合时代

Timely Asynchronous Hierarchical Federated Learning: Age of Convergence

In the traditional vehicular network, computing tasks generated by the
vehicles are usually uploaded to the cloud for processing. However, since task
offloading toward the cloud will cause a large delay, vehicular edge computing
(VEC) is introduced to avoid such a problem and improve the whole system
performance, where a roadside unit (RSU) with certain computing capability is
used to process the data of vehicles as an edge entity. Owing to the privacy
and security issues, vehicles are reluctant to upload local data directly to
the RSU, and thus federated learning (FL) becomes a promising technology for
some machine learning tasks in VEC, where vehicles only need to upload the
local model hyperparameters instead of transferring their local data to the
nearby RSU. Furthermore, as vehicles have different local training time due to
various sizes of local data and their different computing capabilities,
asynchronous federated learning (AFL) is employed to facilitate the RSU to
update the global model immediately after receiving a local model to reduce the
aggregation delay. However, in AFL of VEC, different vehicles may have
different impact on the global model updating because of their various local
training delay, transmission delay and local data sizes. Also, if there are bad
nodes among the vehicles, it will affect the global aggregation quality at the
RSU. To solve the above problem, we shall propose a deep reinforcement learning
(DRL) based vehicle selection scheme to improve the accuracy of the global
model in AFL of vehicular network. In the scheme, we present the model
including the state, action and reward in the DRL based to the specific
problem. Simulation results demonstrate our scheme can effectively remove the
bad nodes and improve the aggregation accuracy of the global model.

因为传统的车联网在上传到云处理中会有延迟，所以为了解决这一问题并提高系统性能，引入了边缘计算，在此基础上采用联邦学习，其中异步联邦学习则采用深度强化学习作为车辆的选择问题模型，模拟结果表明有效提高了全局模型聚合的准确性。