无人机赋能的联邦学习延迟最小化：轨迹设计与资源分配

自适应无人机辅助分层联邦学习：为动态智能物联网优化能量、延迟和容错性

Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT

本文提出了一种自适应无人机辅助分层联邦学习框架，通过联合优化学习配置、带宽分配和设备与无人机关联，以降低全局训练成本并提高动态物联网环境下的鲁棒性。

UAV Communications for Sustainable Federated Learning

Federated learning (FL), invented by Google in 2016, has become a hot research trend. However, enabling FL in wireless networks has to overcome the limited battery challenge of mobile users. In this regard, we propose to apply unmanned aerial vehicle (UAV)-empowered wireless power transfer to enable sustainable FL-based wireless networks. The objective is to maximize the UAV transmit power efficiency, via a joint optimization of transmission time and bandwidth allocation, power control, and the UAV placement. Directly solving the formulated problem is challenging, due to the coupling of variab…

无人机辅助的多任务联邦学习与任务知识共享

UAV-Assisted Multi-Task Federated Learning with Task Knowledge Sharing

本文提出一种无人机辅助的多任务联邦学习方案，通过共享特征提取器和任务注意力机制，在有限带宽下优化带宽分配并采用联盟博弈策略，以提升多任务性能与训练速度。

Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization

Wireless federated learning (FL) is an emerging machine learning paradigm that trains a global parametric model from distributed datasets via wireless communications. This paper proposes a unit-modulus wireless FL (UMWFL) framework, which simultaneously uploads local model parameters and computes global model parameters via optimized phase shifting. The proposed framework avoids sophisticated baseband signal processing, leading to both low communication delays and implementation costs. A training loss bound is derived and a penalty alternating minimization (PAM) algorithm is proposed to minimi…

基于UAV的频谱感知联邦学习：通过SNR加权模型聚合提升准确性

Federated Learning for UAV-Based Spectrum Sensing: Enhancing Accuracy Through SNR-Weighted Model Aggregation

针对无人机（UAV）网络3D频谱感知问题，提出基于联邦学习的方法FedSNR，利用信噪比加权聚合全局模型，在不共享原始数据的前提下保护隐私、降低开销，实验证明其优于传统方法。

无人机网络中面向时延感知的多模态联邦学习

Latency-aware Multimodal Federated Learning over UAV Networks

针对无人机辅助联邦多模态学习的系统时延优化问题，提出结合块坐标下降与连续凸逼近的迭代算法，联合优化多类资源与无人机调度，还给出非凸损失下的收敛分析，实验验证其时延与训练性能更优。

UAV-Aided Multi-Community Federated Learning

In this work, we investigate the problem of an online trajectory design for an Unmanned Aerial Vehicle (UAV) in a Federated Learning (FL) setting where several different communities exist, each defined by a unique task to be learned. In this setting, spatially distributed devices belonging to each community collaboratively contribute towards training their community model via wireless links provided by the UAV. Accordingly, the UAV acts as a mobile orchestrator coordinating the transmissions and the learning schedule among the devices in each community, intending to accelerate the learning pro…

Modeling and Analysis of Intermittent Federated Learning Over Cellular-Connected UAV Networks

Federated learning (FL) is a promising distributed learning technique particularly suitable for wireless learning scenarios since it can accomplish a learning task without raw data transportation so as to preserve data privacy and lower network resource consumption. However, current works on FL over wireless networks do not profoundly study the fundamental performance of FL over wireless networks that suffers from communication outage due to channel impairment and network interference. To accurately exploit the performance of FL over wireless networks, this paper proposes a novel intermittent …

面向无人机赋能联邦学习的数据高效且能量感知的参与者选择

Data-Efficient Energy-Aware Participant Selection for UAV-Enabled Federated Learning

针对无人机边缘联邦学习中的数据冗余与能耗约束，提出DEEPS参与者选择策略，利用SSIM得分和功耗概况选择参与者，提升模型准确率、训练效率与能耗表现。

Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms

Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks ranging from coordinated trajectory planning to cooperative target recognition. However, due to the lack of continuous connections between the UAV swarm and ground base stations (BSs), using centralized ML will be challenging, particularly when dealing with a large volume of data. In this paper, a novel framework is proposed to implement distributed federated learning (FL) algorithms within a UAV swarm that consists of a leading UAV and several following UAVs. Each following UAV trains a l…

安全的无人机辅助联邦学习：一种结合零知识证明的数字孪生驱动方法

Secure UAV-assisted Federated Learning: A Digital Twin-Driven Approach with Zero-Knowledge Proofs

本文提出一种结合数字孪生与零知识联邦学习的无人机辅助FL框架，通过动态分配策略优化路径与功率，利用凸优化降低29.6%能耗，同时增强系统安全性、监控能力与可扩展性。

Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities

Unmanned aerial vehicles (UAVs), or say drones, are envisioned to support extensive applications in next-generation wireless networks in both civil and military fields. Empowering UAVs networks intelligence by artificial intelligence (AI) especially machine learning (ML) techniques is inevitable and appealing to enable the aforementioned applications. To solve the problems of traditional cloud-centric ML for UAV networks such as privacy concern, unacceptable latency, and resource burden, a distributed ML technique, \textit(i.e.), federated learning (FL), has been recently proposed to enable mu…

Delay Minimization for Federated Learning Over Wireless Communication Networks

In this paper, the problem of delay minimization for federated learning (FL) over wireless communication networks is investigated. In the considered model, each user exploits limited local computational resources to train a local FL model with its collected data and, then, sends the trained FL model parameters to a base station (BS) which aggregates the local FL models and broadcasts the aggregated FL model back to all the users. Since FL involves learning model exchanges between the users and the BS, both computation and communication latencies are determined by the required learning accuracy…

聚类联邦学习中全变分最小化的分析

Analysis of Total Variation Minimization for Clustered Federated Learning

本文针对联邦学习中本地数据统计异质性问题，分析了广义全变分最小化（GTVMin）方法，并在聚类假设下推导出其解与簇内平均值偏差的上界，为该方法在聚类联邦学习中的有效性和鲁棒性提供了理论依据。

UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach

We investigate training machine learning (ML) models across a set of geo-distributed, resource-constrained clusters of devices through unmanned aerial vehicles (UAV) swarms. The presence of time-varying data heterogeneity and computational resource inadequacy among device clusters motivate four key parts of our methodology: (i) stratified UAV swarms of leader, worker, and coordinator UAVs, (ii) hierarchical nested personalized federated learning (HN-PFL), a distributed ML framework for personalized model training across the worker-leader-core network hierarchy, (iii) cooperative UAV resource p…

用于降低无人机网络通信成本和能耗的去中心化联邦学习方法

Decentralized federated learning methods for reducing communication cost and energy consumption in UAV networks

本文针对无人机网络中传统联邦学习依赖中央服务器的问题，提出交换式联邦学习和交替式联邦学习两种去中心化聚合方法，通过控制本地训练轮数和通信来降低能耗与通信成本，并在仿真中验证其优于基准方法。

基于动态任务知识共享的高效无人机集群多任务联邦学习

Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharing

本文针对无人机集群在资源受限下多任务联邦学习效率低的问题，提出动态任务注意力机制与任务亲和度度量，通过双层优化联合分配通信计算资源，实现任务知识共享并揭示能耗与性能的权衡。

通过节能无人机通信实现个性化联邦学习

Personalized Federated Learning by Energy-Efficient UAV Communications

针对无人机辅助联邦学习中数据异构和能耗问题，提出全局共享骨干与本地个性化头部分离的架构，并设计基于梯度范数的调度策略，在降低能耗的同时提升学习精度。

一种用于增强无人机隐私与安全的新型联邦学习入侵检测系统

A Novel Federated Learning-Based IDS for Enhancing UAVs Privacy and Security

本文提出了一种基于联邦学习的入侵检测系统FL-IDS，通过去中心化协作训练保护无人机网络数据隐私，降低计算存储成本，并结合BTSC方法在低攻击比例下提升性能，为UAV网络提供了隐私感知的安全方案。

面向非IID数据下无人机赋能联邦边缘学习的可靠参与研究

Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data

针对无人机赋能联邦学习中数据非IID、无意断连及投毒攻击影响收敛的问题，提出兼顾速度与可靠性、剔除恶意无人机的客户端选择方案，实验验证其在多攻击场景下收敛性与准确率优于基线方法。

Completion Time Minimization of Fog-RAN-Assisted Federated Learning With Rate-Splitting Transmission

This work studies federated learning (FL) over a fog radio access network, in which multiple internet-of-things (IoT) devices cooperatively learn a shared machine learning model by communicating with a cloud server (CS) through distributed access points (APs). Under the assumption that the fronthaul links connecting APs to CS have finite capacity, a rate-splitting transmission at IoT devices (IDs) is proposed which enables hybrid edge and cloud decoding of split uplink messages. The problem of completion time minimization for FL is tackled by optimizing the rate-splitting transmission and fron…

UTM系统中基于联邦学习的无人机协同宽带频谱感知与调度

Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems

提出一种基于联邦学习的无人机协同宽带频谱感知与调度框架，集成数据生成、协同推理与强化学习调度，并构建仿真环境验证其有效性。

FedSCAM（联邦锐度感知最小化与聚类聚合与调制）：抗欺诈的SAM用于异构环境中的鲁棒联邦优化

FedSCAM (Federated Sharpness-Aware Minimization with Clustered Aggregation and Modulation): Scam-resistant SAM for Robust Federated Optimization in Heterogeneous Environments

FedSCAM通过动态调整扰动半径和聚合权重，提升联邦优化在异构环境中的鲁棒性和收敛速度。

Generalized Federated Learning via Sharpness Aware Minimization

Federated Learning (FL) is a promising framework for performing privacy-preserving, distributed learning with a set of clients. However, the data distribution among clients often exhibits non-IID, i.e., distribution shift, which makes efficient optimization difficult. To tackle this problem, many FL algorithms focus on mitigating the effects of data heterogeneity across clients by increasing the performance of the global model. However, almost all algorithms leverage Empirical Risk Minimization (ERM) to be the local optimizer, which is easy to make the global model fall into a sharp valley and…

基于广义全变差最小化的聚类联邦学习

Clustered Federated Learning via Generalized Total Variation Minimization

本文提出将联邦学习表述为广义全变差最小化，并设计一种完全分散的原始-对偶算法，在理论上界定了与聚类基准方法的偏差，从而有效训练个性化模型。

物联网网络中无人机联邦学习的部分可观测传输控制

Partially-Observable Transmission Control for UAV-Enabled Federated Learning in IoT Networks

针对物联网无人机联邦学习的传输干扰问题，提出公平共识双层优化算法，结合阈值与功率控制提升数据包交付率，性能优于基线策略。

面向人机协作领域的无人机近端控制与联邦学习

Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains

本文提出一种基于LSTM深度神经网络与联邦学习的无人机动作识别与控制方法，解决多机器人场景中的遮挡和分布式训练问题，真实实验准确率超96%。

联邦软聚类：基于广义全变差最小化

Federated Soft Clustering via Generalized Total Variation Minimization

针对联邦学习中各设备持有私有数据并拟合个性化高斯混合模型的软聚类问题，提出广义全变差最小化框架，比较三种差异度量，并给出优化算法与收敛保证，评估了对数据异质性的鲁棒性。

一种面向拜占庭鲁棒联邦学习的 Huber 损失最小化方法

A Huber Loss Minimization Approach to Byzantine Robust Federated Learning

本文提出一种基于 Huber 损失最小化的新型聚合器以防御联邦学习中的对抗攻击，理论证明其在 i.i.d 下对受攻击比例 $ε$ 依赖最优且无需精确了解 $ε$，并支持不等数据量，同时扩展至 non-i.i.d 数据场景。

Time Minimization in Hierarchical Federated Learning

Federated Learning is a modern decentralized machine learning technique where user equipments perform machine learning tasks locally and then upload the model parameters to a central server. In this paper, we consider a 3-layer hierarchical federated learning system which involves model parameter exchanges between the cloud and edge servers, and the edge servers and user equipment. In a hierarchical federated learning model, delay in communication and computation of model parameters has a great impact on achieving a predefined global model accuracy. Therefore, we formulate a joint learning and…

Characterization of the Global Bias Problem in Aerial Federated Learning

Unmanned aerial vehicles (UAVs) mobility enables flexible and customized federated learning (FL) at the network edge. However, the underlying uncertainties in the aerial-terrestrial wireless channel may lead to a biased FL model. In particular, the distribution of the global model and the aggregation of the local updates within the FL learning rounds at the UAVs are governed by the reliability of the wireless channel. This creates an undesirable bias towards the training data of ground devices with better channel conditions, and vice versa. This paper characterizes the global bias problem of a…

基于广义总变分最小化的最小二乘联邦学习的高维分析

A Large-Dimensional Analysis of Least-Squares Federated Learning via Generalized Total Variation Minimization

该研究针对结合最小二乘模型的广义总变分最小化，通过高维分析揭示联邦学习中协作与偏差的权衡，利用随机矩阵理论推导性能表达式并经实验验证，为异构节点联邦学习的正则化平衡提供理论支撑。

用于可扩展且具弹性的无人机避撞的分层联邦图注意力网络

Hierarchical Federated Graph Attention Networks for Scalable and Resilient UAV Collision Avoidance

针对大规模多无人机避撞的实时性、对抗弹性与隐私保护平衡问题，提出三层分层联邦图注意力网络架构，实现拜占庭容错、低延迟与可扩展性，支持500架无人机场景且碰撞率<2.0%。

局部估计的全局扰动优于局部扰动用于联邦锐度感知最小化

Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization

针对联邦学习中数据异构性导致全局模型损失景观锐化的问题，提出FedLESAM算法，通过客户端局部估计全局扰动方向并减少反向传播次数，在提升性能的同时加速了联邦锐度感知最小化。

在低空网络中实现隐私保护图像传输：基于Swin Transformer的框架与联邦学习

On Privacy-Preserving Image Transmission in Low-Altitude Networks: A Swin Transformer-Based Framework with Federated Learning

本文提出基于联邦学习的语义通信框架，利用Swin Transformer提取多尺度语义特征，提升低空网络中图像传输的效率与隐私保护，实验显示PSNR提升达5.7dB。

DP$^2$-FedSAM：通过个性化锐度感知最小化增强差分隐私联邦学习

DP$^2$-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization

本文提出DP$^2$-FedSAM，结合个性化部分模型共享与锐度感知最小化，在差分隐私联邦学习中减轻噪声与裁剪影响，提升模型效用，尤其在数据异构场景下改善隐私-效用权衡。

即插即学：基于模型智能电网的联邦智能

Plug In and Learn: Federated Intelligence over a Smart Grid of Models

提出一种模型无关的联邦学习方法，借鉴智能电网机制，通过图正则化器协调统计相似的本地模型在公共无标签数据集上产生相似预测，支持多种参数与非参数模型，且兼容标准机器学习库。

AltGDmin：交替梯度下降与最小化用于部分解耦（联邦）优化

AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization

提出AltGDmin框架，通过交替梯度下降与最小化加速部分解耦优化，适用于低秩、鲁棒、张量及离散问题，并在联邦设置中实现通信高效。

物联网中参与式联邦学习的能量最小化：博弈论分析

Energy Minimization for Participatory Federated Learning in IoT Analyzed via Game Theory

针对物联网中参与式联邦学习，提出基于博弈论的分布式能量最小化方法，理论结合实验验证其有效性，并指出高无政府状态代价需激励机

面向客户端漂移最小化的分布鲁棒联邦学习

Distributionally Robust Federated Learning with Client Drift Minimization

针对联邦学习中数据异构导致的性能不公平与效率低问题，提出结合分布鲁棒优化和动态正则化的DRDM算法，可减少通信轮次、提升最差情况准确率，还能自适应调整局部更新以优化能耗。

Uncertainty Minimization for Personalized Federated Semi-Supervised Learning

Since federated learning (FL) has been introduced as a decentralized learning technique with privacy preservation, statistical heterogeneity of distributed data stays the main obstacle to achieve robust performance and stable convergence in FL applications. Model personalization methods have been studied to overcome this problem. However, existing approaches are mainly under the prerequisite of fully labeled data, which is unrealistic in practice due to the requirement of expertise. The primary issue caused by partial-labeled condition is that, clients with deficient labeled data can suffer fr…

无线联邦学习中的收敛加速：一种 Stackelberg 博弈方法

Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach

针对无线联邦学习收敛慢的问题，提出 Stackelberg 博弈框架，联合优化全局损失与时延，并设计基于 AoU 的设备选择算法以提升收敛速率和子信道利用率。

Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones

Air access networks have been recognized as a significant driver of various Internet of Things (IoT) services and applications. In particular, the aerial computing network infrastructure centered on the Internet of Drones has set off a new revolution in automatic image recognition. This emerging technology relies on sharing ground truth labeled data between Unmanned Aerial Vehicle (UAV) swarms to train a high-quality automatic image recognition model. However, such an approach will bring data privacy and data availability challenges. To address these issues, we first present a Semi-supervised …

Service Delay Minimization for Federated Learning over Mobile Devices

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 delay efficient FL (SDEFL) scheme over mobile devices. Unlike traditional communication efficient FL, which regards wireless communications as the bottleneck, we find that under many situations, the local computing delay is comparable to the communication delay during the FL training process, given the development of high-speed wireless transmission techniques. Thus, the service delay in FL should be computing delay + communi…

支持模型拆分的联邦边缘学习训练时延最小化

Training Latency Minimization for Model-Splitting Allowed Federated Edge Learning

针对联邦学习中客户端算力不足的问题，提出支持模型拆分的SFL框架，通过回归转化与交替优化算法求解训练时延最小化问题，实验验证了其有效性与性能提升。

分层无线网络上的联邦学习：通过子模型划分最小化训练延迟

Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioning

提出分层独立子模型训练（HIST）方法，通过划分全局模型为不相交分区并利用空中计算增强边缘聚合，在保证准确率的同时大幅降低训练延迟与通信成本。

联邦学习中的动态正则化锐度感知最小化：逼近全局一致性与平滑景观

Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape

本文提出联邦学习算法 FedSMOO，通过动态正则化器与全局锐度感知最小化相结合，在优化一致性和泛化平坦性之间取得平衡，实现快速收敛和低泛化误差。

Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks

Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks. However, due to the ever-increasing number of UEs and the complicated administrative work it brings, it is desirable to switch the PFL algorithm from its conventional two-layer framework to a multiple-layer one. In this paper, we propose hierarchical PFL (HPFL), an algorithm for deploying PFL over massive MEC networks. The UEs in HPFL are divided into multiple clusters, and the UE…

Scheduling Algorithms for Federated Learning with Minimal Energy Consumption

Federated Learning (FL) has opened the opportunity for collaboratively training machine learning models on heterogeneous mobile or Edge devices while keeping local data private.With an increase in its adoption, a growing concern is related to its economic and environmental cost (as is also the case for other machine learning techniques).Unfortunately, little work has been done to optimize its energy consumption or emissions of carbon dioxide or equivalents, as energy minimization is usually left as a secondary objective.In this paper, we investigate the problem of minimizing the energy consump…

FedGen：面向序列数据的可泛化联邦学习

FedGen: Generalizable Federated Learning for Sequential Data

本文提出联邦学习框架 FedGen，使客户端在无训练分布先验知识的情况下协作区分虚假特征与不变特征，从而提升模型泛化能力，并在多个真实数据集上实现准确率提升超过24%。

FedEM：联邦学习中兼顾效用保护的隐私保护框架

FedEM: A Privacy-Preserving Framework for Concurrent Utility Preservation in Federated Learning

本文提出FedEM算法，通过自适应噪声注入引入受控扰动，在联邦学习中有效缓解梯度泄露攻击，同时保持模型精度，实现隐私保护与效用保留的稳健平衡。

一种通过加权动量和尖锐意识最小化实现快速且平坦的联邦学习方法

A Fast and Flat Federated Learning Method via Weighted Momentum and Sharpness-Aware Minimization

FedWMSAM通过加权动量和尖锐意识最小化解决非IID联邦学习中的局部-全局曲率不一致和动量-回声振荡问题，提升模型收敛速度和泛化能力。

Bayesian Variational Federated Learning and Unlearning in Decentralized Networks

Federated Bayesian learning offers a principled framework for the definition of collaborative training algorithms that are able to quantify epistemic uncertainty and to produce trustworthy decisions. Upon the completion of collaborative training, an agent may decide to exercise her legal "right to be forgotten", which calls for her contribution to the jointly trained model to be deleted and discarded. This paper studies federated learning and unlearning in a decentralized network within a Bayesian framework. It specifically develops federated variational inference (VI) solutions based on the d…

面向隐私保护型脑机接口的联邦运动想象分类

Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces

针对EEG脑机接口中多用户数据隐私与运动想象分类精度难以兼顾的问题，提出FedBS联邦学习方法，通过局部批次特定批归一化和SAM优化提升泛化，实验性能超越六种联邦方法及集中式训练。