Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication cost, robustness to heterogeneous data, and support for diverse device capabilities. However, there is no unified framework that addresses all these problems together. This paper studies the challenges and opportunities of exploiting pre-trained Transformer models in FL. In particular, we propose to efficiently adapt such pre-trained models by injecting a novel attention-based adapter module at each transformer block that both modulates the forward pass and makes an early prediction. Training only the lightweight adapter by FL leads to fast and communication-efficient learning even in the presence of heterogeneous data and devices. Extensive experiments on standard FL benchmarks, including CIFAR-100, FEMNIST and SpeechCommandsv2 demonstrate that this simple framework provides fast and accurate FL while supporting heterogenous device capabilities, efficient personalization, and scalable-cost anytime inference.

当可用的硬件无法满足内存和计算要求以有效地训练高性能的机器学习模型时，需要妥协训练质量或模型复杂性。在联合学习（FL）中，节点是比传统服务器级硬件更具限制的数量级，并且通常是电池供电的，严重限制了可以在此范式下训练的模型的复杂性。尽管大多数研究都集中在设计更好的聚合策略上以提高收敛速度并减轻FL的沟通成本，但更少的努力致力于加快设备培训。这样的阶段重复数百次（即每回合）并可能涉及数千个设备，这是培训联合模型所需的大部分时间，以及客户端的全部能源消耗。在这项工作中，我们介绍了第一个研究在FL工作负载中培训时间引入稀疏性时出现的独特方面的研究。然后，我们提出了Zerofl，该框架依赖于高度稀疏的操作来加快设备训练。与通过将最先进的稀疏训练框架适应FL设置相比，接受Zerofl和95％稀疏性训练的模型高达2.3％的精度。

联邦学习（FL）已成为一种前瞻性解决方案，可促进对高性能的集中模型的培训，而不会损害用户的隐私。尽管成功，但目前的研究受到了在实验初期建立现实的大规模FL系统的可能性的限制。仿真可以帮助加速这一过程。为了促进异构客户的有效可扩展的FL模拟，我们设计和实施ProteA，这是使用FL框架花朵在联合系统中灵活且轻巧的客户型分析组件。它允许自动收集系统级统计信息并估算每个客户所需的资源，从而以资源感知方式运行模拟。结果表明，我们的设计成功地增加了1.66 $ \ times $ $更快的壁挂时间和2.6 $ \ times $更好的GPU利用率的平行性，这可以对异构客户进行大规模实验。

联邦学习（FL）最近由于其在保留隐私而使用分散数据的能力，最近引起了人们的关注。但是，这也提出了与参与设备的异质性有关的其他挑战，无论是在其计算能力和贡献数据方面。同时，神经体系结构搜索（NAS）已成功用于集中式数据集，从而产生了最新的结果，从而获得了受限（硬件意识）和不受约束的设置。但是，即使是在NAS和FL的交集的最新工作，也假定了与数据中心硬件的均匀计算环境，并且无法解决使用受约束，异质设备的问题。结果，在联合环境中对NAS的实际用法仍然是我们在工作中解决的一个空旷的问题。我们设计我们的系统Fedoras，在处理具有非IID分布数据的不同功能的设备时发现和培训有希望的体系结构，并提供了其在不同环境中有效性的经验证据。具体而言，我们在跨越三种不同模式（视觉，语音，文本）的数据集中评估了Fedoras，并且与最先进的联合解决方案相比，其性能更好，同时保持资源效率。

支持麦克风的设备的无处不在导致在边缘生产大量未标记的音频数据。自我监督学习（SSL）和联合学习（FL）的整合到一个连贯的系统中，可以提供数据隐私保证，同时还可以提高语音表示的质量和稳健性。在本文中，从算法，硬件和系统限制的角度来看，我们对FL场景下的培训语音SSL模型的可行性和复杂性提供了首个系统研究。尽管它们的组合具有很高的潜力，但我们发现现有的系统限制和算法行为使SSL和FL系统几乎无法构建。然而，至关重要的是，我们的结果表明了特定的绩效瓶颈和研究机会，这将使这种情况得到逆转。尽管我们的分析表明，鉴于硬件的现有趋势，混合SSL和FL语音系统要等到2027年才能可行。我们认为，这项研究可以成为加速工作以提早达到这一里程碑的路线图。

尽管结果令人印象深刻，但深度学习的技术还引起了经常在数据中心进行的培训程序引起的严重隐私和环境问题。作为回应，已经出现了集中培训的替代方案，例如联邦学习（FL）。也许出乎意料的是，FL开始在全球范围内部署，这些公司必须遵守源自倡导隐私保护的政府和社会团体的新法律要求和政策。 \ textit {但是，与FL有关的潜在环境影响仍然不清楚和未开发。本文提供了有关佛罗里达碳足迹的首次系统研究。然后，我们将FL的碳足迹与传统的集中学习进行了比较。我们的发现表明，根据配置，FL可以比集中的机器学习高达两个数量级。但是，在某些情况下，由于嵌入式设备的能源消耗减少，它可以与集中学习相提并论。我们使用FL进行了不同类型的数据集，设置和各种深度学习模型的广泛实验。最后，我们强调并将报告的结果与FL的未来挑战和趋势联系起来，以减少其环境影响，包括算法效率，硬件能力和更强的行业透明度。

联合学习（FL）作为边缘设备的有希望的技术，以协作学习共享预测模型，同时保持其训练数据，从而解耦了从需要存储云中的数据的机器学习的能力。然而，在规模和系统异质性方面，FL难以现实地实现。虽然有许多用于模拟FL算法的研究框架，但它们不支持在异构边缘设备上进行可扩展的流程。在本文中，我们呈现花 - 一种全面的FL框架，通过提供新的设施来执行大规模的FL实验并考虑丰富的异构流程来区分现有平台。我们的实验表明花卉可以仅使用一对高端GPU在客户尺寸下进行FL实验。然后，研究人员可以将实验无缝地迁移到真实设备中以检查设计空间的其他部分。我们认为花卉为社区提供了一个批判性的新工具，用于研究和发展。

Modelling and forecasting real-life human behaviour using online social media is an active endeavour of interest in politics, government, academia, and industry. Since its creation in 2006, Twitter has been proposed as a potential laboratory that could be used to gauge and predict social behaviour. During the last decade, the user base of Twitter has been growing and becoming more representative of the general population. Here we analyse this user base in the context of the 2021 Mexican Legislative Election. To do so, we use a dataset of 15 million election-related tweets in the six months preceding election day. We explore different election models that assign political preference to either the ruling parties or the opposition. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods. These results demonstrate that analysis of public online data can outperform conventional polling methods, and that political analysis and general forecasting would likely benefit from incorporating such data in the immediate future. Moreover, the same Twitter dataset with geographical attributes is positively correlated with results from official census data on population and internet usage in Mexico. These findings suggest that we have reached a period in time when online activity, appropriately curated, can provide an accurate representation of offline behaviour.

Content moderation is the process of screening and monitoring user-generated content online. It plays a crucial role in stopping content resulting from unacceptable behaviors such as hate speech, harassment, violence against specific groups, terrorism, racism, xenophobia, homophobia, or misogyny, to mention some few, in Online Social Platforms. These platforms make use of a plethora of tools to detect and manage malicious information; however, malicious actors also improve their skills, developing strategies to surpass these barriers and continuing to spread misleading information. Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems. In response to this recent ongoing issue, this paper presents an innovative approach to address this linguistic trend in social networks through the simulation of different content evasion techniques and a multilingual Transformer model for content evasion detection. In this way, we share with the rest of the scientific community a multilingual public tool, named "pyleetspeak" to generate/simulate in a customizable way the phenomenon of content evasion through automatic word camouflage and a multilingual Named-Entity Recognition (NER) Transformer-based model tuned for its recognition and detection. The multilingual NER model is evaluated in different textual scenarios, detecting different types and mixtures of camouflage techniques, achieving an overall weighted F1 score of 0.8795. This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content on social networks, making the fight against information disorders more effective.

In this paper, we present an evolved version of the Situational Graphs, which jointly models in a single optimizable factor graph, a SLAM graph, as a set of robot keyframes, containing its associated measurements and robot poses, and a 3D scene graph, as a high-level representation of the environment that encodes its different geometric elements with semantic attributes and the relational information between those elements. Our proposed S-Graphs+ is a novel four-layered factor graph that includes: (1) a keyframes layer with robot pose estimates, (2) a walls layer representing wall surfaces, (3) a rooms layer encompassing sets of wall planes, and (4) a floors layer gathering the rooms within a given floor level. The above graph is optimized in real-time to obtain a robust and accurate estimate of the robot's pose and its map, simultaneously constructing and leveraging the high-level information of the environment. To extract such high-level information, we present novel room and floor segmentation algorithms utilizing the mapped wall planes and free-space clusters. We tested S-Graphs+ on multiple datasets including, simulations of distinct indoor environments, on real datasets captured over several construction sites and office environments, and on a real public dataset of indoor office environments. S-Graphs+ outperforms relevant baselines in the majority of the datasets while extending the robot situational awareness by a four-layered scene model. Moreover, we make the algorithm available as a docker file.