Author: camila

The seminar will be titled “Federated Learning at the Edge: Addressing Data Heterogeneity in IoT Systems”

Federated Learning (FL) and Edge AI are important building blocks of scalable and privacy-preserving intelligence in Internet of Things (IoT)-based systems. However, real-world deployments are inherently affected by data heterogeneity (non-IID distributions) across settings, which significantly degrades model performance and convergence. In this talk, we present a system-oriented approach to edge intelligence, combining on-device inference, federated training, and architecture-level solutions to address heterogeneity. We begin with edge-native AI pipelines for real-time sensing and inference, highlighting how local processing reduces latency and communication overhead. We then discuss federated transfer learning strategies that enable collaborative model training across distributed clients while preserving data locality. Finally, a novel federated architecture based on a client-shared latent space, which improves robustness to non-IID data by aligning semantic representations across clients while reducing communication costs.

The speaker

Ivan Zyrianoff received the B.S. degree in computer science and the M.S. degree in information engineering from the Federal University of ABC, Santo André, Brazil, in 2017 and 2019, respectively, and the Ph.D. degree from the University of Bologna, Bologna, Italy, in 2024. He is a Research Fellow from the University of Bologna, Bologna, Italy, and a member of the IoT-Prism Lab. His current research topics encompass interoperability for the Internet of Things, edge computing and intelligence, and proactive caching.

The talk, titled “Energy-efficient Federated Transfer Learning for Privacy-Preserving Energy-Usage Forecasting,” will take place at the opening of the workshop, scheduled for 2:00 p.m. on May 27, 2026.

Read the abstract of the presentation:

Accurate forecasting of residential energy demand is
increasingly critical due to growing household electrification, renewable
integration, climate variability, and diverse consumption patterns.
Centralized forecasting models have privacy issues and limitations in
dynamic environments. This keynote introduces PEFEDTL, a personalized
federated transfer learning (FTL) framework for multivariate energy
forecasting in smart homes. It combines temporal convolutional networks
with a global attention module and cluster-based personalization. Many
other existing FTL approaches largely overlook device heterogeneity and
resource constraints, leading to suboptimal efficiency and limited
applicability in real-world edge environments. To address this gap, we
discuss possible approaches for energy-efficient FTL and present
Resource-Aware Federated Transfer Learning (RA-FTL), a framework that
adapts both model architecture and resource utilization to heterogeneous
client capabilities.

Bio – Prof. Dr. Torsten Braun

Head of the Communication and Distributed Systems (CDS) research group atthe Institute of Computer Science, University of Bern, where he has been a
full professor since 1998. He got the Ph.D. degree from University of
Karlsruhe (Germany) in 1993. From 1994 to 1995, he was a guest scientist at
INRIA Sophia-Antipolis (France). From 1995 to 1997, he worked at the IBM
European Networking Centre Heidelberg (Germany) as a project leader and
senior consultant. He has been a vice president of the SWITCH (Swiss
Research and Education Network Provider) Foundation from 2011 to 2019. He has been a Director of the Institute of Computer Science at University of
Bern (INF) between 2007 and 2011, and from 2019 to 2021. Currently, he
serves as a Director of Studies at INF. He has been panel member of several
national research funding organizations such as Switzerland, Luxembourg,
Denmark, Finland, Norway, and Sweden. He has been supervising more than 40 PhD students, several of them with joint PhD supervision agreements with Unicamp and UFPA, Belem (Brazil).

How can computing help address today’s environmental challenges?

The central theme of CSBC 2026, “Digital Transformation for a World in the Midst of a Climate Emergency,” invites critical reflection on the role of digital technologies in mitigating environmental impacts and building a more sustainable future.

The event will feature technical sessions, panel discussions, and lectures by national and international experts, providing a qualified space for interdisciplinary discussion on technological innovation and sustainability.

Learn more about CSBC 2026

The presentation will be titled “Security Analysis of Confidential Computing and Anonymous Communication”

Confidential Computing extends security guarantees to the data processing stage, providing confidentiality and integrity during execution through Trusted Execution Environments (TEEs) anchored in specialized hardware.

Anonymous Communication, in turn, protects the identity of the parties and the metadata associated with interactions—information that remains exposed even when the content is protected by encryption. This presentation discusses the technical foundations of both paradigms and their security properties under a realistic threat model, demonstrating that their guarantees are conditional and depend on a chain of trust based on hardware, remote attestation, and proper separation of responsibilities among components.

As a case study, we analyze Meta’s Private Processing for WhatsApp, which combines TEEs, Oblivious HTTP, and immutable logs to enable AI features while preserving user privacy, illustrating the complementarity between Confidential Computing, Anonymous Communication, and End-to-End Encryption (E2EE).

The speaker

Professor at the Federal University of Rio Grande do Sul (UFRGS). Member of the Brazilian Computer Society (SBC). He holds a bachelor’s degree in Electrical Engineering from the Federal University of Rio Grande do Sul (2002), a master’s degree in Computer Science (2010), and a Ph.D. (2015) from the same university. He completed a sandwich PhD program (2011–2012) at Cisco Systems (USA). He completed postdoctoral research at the Federal University of Pará (2016). Experienced in the field of Computer Networks and Distributed Systems, with an emphasis on Computer Network Management and Security.

 

The Brazilian Computer Society (SBC) Conference is an annual event organized by the Brazilian Computer Society (SBC). The 46th edition of the Conference will be held in Gramado, Rio Grande do Sul, from July 19 to 23, 2026.

Over more than four decades, the CSBC has become the most important national scientific event in computer science. The CSBC’s excellent reputation is evident in the quality and significant number of paper submissions to its ten main sub-events and sixteen satellite events.

Other characteristics, such as the diversity and breadth of activities carried out, the relevance of the topics addressed, and the professionalism of its organization, have contributed to CSBC’s consolidation in the calendar of national scientific events and its emergence as the most important event in the field of computer science in South America.

With over four decades of history, CSBC has established itself as the leading scientific forum in the field in the country, bringing together approximately two thousand participants annually, including researchers, students, and professionals from Brazil and abroad.

The event is organized by the Brazilian Computer Society (SBC), the leading scientific entity in the field in Brazil. For this edition, the organizers are researchers Weverton Cordeiro and Alberto Egon Schaeffer Filho (UFRGS), affiliated with INCT ICoNIoT.

Save the date and follow updates via social media and the ICoNIoT newsletter.

 

The research is supervised by researcher Luiz Fernando Bittencourt

Researchers in the fields of IoT and computer networks are focused on addressing a critical challenge of contemporary life: maintaining fast, uninterrupted connectivity for users who are constantly on the move.

This effort centres on Edge Computing, in which processing is distributed across multiple locations.

The core of the research by ICoNIoT researcher Marcelo Araújo, carried out as part of a postdoctoral fellowship under the supervision of Professor Luiz Fernando Bittencourt (UNICAMP), aims to ensure that the dataset that would normally be processed in the cloud accompanies the user during their daily journeys to work, leisure activities, etc. The primary objective is to improve latency and the overall user experience, ensuring that response times remain as low and fast as possible.

The challenge of edge environments

Despite the need for proximity, there is a major issue requiring research: environments located at the network edge – which may be mini data centres or even routers – are less robust and have less computing power. The research therefore aims to find the best possible way to carry out this data handoff between these environments.

The proposed solution involves creating an algorithm that can be adapted for this specific purpose, capable of identifying when the user is on the move.

Deep learning in decision-making

Araújo’s postdoctoral project combines concepts from Artificial Intelligence (AI) with the work he had already been developing. The focus is on deep learning to enable the computer system to make the best decisions autonomously.

The system evaluates a range of data and metrics to decide how to manage mobility, including:

• Predicting the user’s mobility;
• Checking latency;
• The user’s distance;
• Assessing the infrastructure near the user, particularly if it is congested.

The use of techniques such as DRL (Deep Reinforcement Learning) increases the flexibility of the metrics that the computer system will evaluate during the decision-making process.

Simulations to Overcome Limitations

One of the major obstacles in this type of project is the high cost involved in carrying out hyper-realistic simulations. Researchers get round this limitation in the early stages by using simulators that incorporate accurate features of the real environment. Furthermore, it is possible to model the behaviour and actions that a user would carry out in their daily life. In the case of Araújo’s project, a synthetic map of the city of Athens was created. This map serves to execute the system’s logic and run a simulation that approximates a real environment.

 

In his presentation, entitled ‘Enabling Real-Time Systems & AI at the Edge’, Dr Dave Cavalcanti, Senior Engineer at Intel, will examine the architectural and system-level fundamentals required to enable deterministic real-time computing in conjunction with AI applications on modern edge platforms.

He will focus on time-coordinated computing, mixed-criticality workloads and the convergence of computing and networking as key factors for next-generation cyber-physical systems.

The presentation will discuss hardware, software and networking features, such as Time-Sensitive Networking, benchmarking tools and their role in enabling AI-enhanced real-time systems across various vertical markets.

The speaker

Dave Cavalcanti is a senior engineer at Intel Corporation, with extensive experience in distributed networking systems, connectivity, industry standards and ecosystems. He also serves as chairman of the Avnu Alliance, an industry forum that promotes standards and certification programmes to enable deterministic real-time performance based on interoperable Time-Sensitive Networking (TSN) devices and converged networks.

He obtained a PhD in Computer Science and Engineering in 2006 from the University of Cincinnati, a Master’s degree in Computer Science and a Bachelor’s degree in Electronic Engineering from UFPE in Brazil. He has published over 50 peer-reviewed articles and holds more than 125 granted patents.

In the research project led by Professor Francisco Airton (UFPI), he and his students Iure Fé (PhD), José Miqueias (MSc) and Lucas Lopes (MSc) are developing analytical models based on Petri nets, which are used to mathematically represent any distributed system. They create a diagram describing the system and perform probabilistic calculations to obtain various metrics, using a specific tool for this purpose.

Airton believes that these models can also be interpreted as digital twins. Based on this, the group selects a specific information system — in the case of the PhD student, Kubernetes — and constructs Petri net models that represent the deployment of Kubernetes. After modelling this system, they integrate the model with software capable of running it and monitoring Kubernetes in real time.

The master’s students, meanwhile, use other monitored systems: one works with camera monitoring, and another with a drone simulator.

The team is preparing papers for this year’s SBRC, describing how the digital twin platform can outperform different types of autoscaling. In this case, the system tests different configurations and simulates ‘what-if’ scenarios, which allows the best option to be identified before applying it in the real environment.

In traditional client-server architecture, Kubernetes operates alongside servers and can connect to any client, including IoT devices. On these devices, a set of sensors generates data that varies depending on the context, such as the time of day or traffic flow. This variation in demand requires the Kubernetes deployment configuration to be adjusted dynamically. This is where K8s-DT comes into play, predicting the best new configuration to be implemented.

 

Oscar Mauricio Caicedo Rendon, one of the members of the international research network of INCT ICoNIoT, will release his book “Machine Learning for Network Management” on April 26, 2026. The book is being published by Springer Nature and can be purchased as a complete volume or by individual chapters.

“Machine Learning for Network Management” was written as a guide text for undergraduate students in last year,  master’s and doctoral students in computer science and related areas who are interested in studying the application of machine learning in networks, introducing them to the world of algorithms that are useful for solving network management problems.

Book structure

The book is divided into three parts. The first is conceptual, covering ML and network management, and presenting a chronology of ML in network management from the 1950s up to 2024/2025, when it reaches generative AI.

The second part deals specifically with network configuration, covering elements such as performance, security, configuration failures, and the definition of algorithms for each of these areas. Algorithms and code are presented, along with their computational complexity and use cases.

The third and final part presents, in greater depth, an example application of generative AI for network management. It introduces a problem that can be addressed using generative AI, aiming to provide a current, up-to-date case.

In addition to code examples, each chapter presents practical exercises and their solutions, as well as slides that professors can use and customize as they wish.

The book connects with several research efforts developed within ICoNIoT.

About the researcher

Oscar Mauricio Caicedo is a professor at the Faculty of Electronic Engineering and Telecommunications at Universidad del Cauca, Colombia, where he is a member of the Telematics Engineering Group. He earned his PhD at UFRGS under the supervision of researcher Lisandro Granville. When he returned to Colombia in 2015, he began working in the field of network management using machine learning algorithms with Professor Nelson Fonseca—well before the boom in generative AI that has been observed. 

In 2018, Prof. Caicedo was already publishing the first results of his research in ML for network management, and he continues to do so in 2026. His book draws on this entire experience of more than ten years of research, using reinforcement learning, deep reinforcement learning, federated learning, explainable artificial intelligence, and large/small language models, among others for solving problems in faults, configuration, performance, and security in communication networks.

To learn more, visit the book’s page on the Springer Nature website

 

At the beginning of the year, the OpenRAN@Brasil Programme announced the results of the Call for 5G Open RAN Applications and Hosts, an initiative aimed at promoting innovative solutions based on open radio access networks. The selected proposals aim to boost the development of 5G Open RAN applications in areas of strategic importance to the country.

Of the six proposals selected, three are coordinated by researchers from INCT ICoNIoT. They are: GreenRAN: Sustainable Open RAN for Smart Agriculture and Campuses/Coordinator: Eduardo Cerqueira (UFPA); OpenHealth5G: Open 5G Networking for Digital Health/Coordinator: Juliano Wickboldt (UFRGS); and Sentinel-5GO: Collaborative Security and Environmental Monitoring in 5G Open RAN Networks/Coordinator: Augusto Neto (UFRN).

A fourth proposal, entitled CampusRAN: Intelligent Campus with Open Radio Access Network and coordinated by Dianne Scherly (UFF), involves the participation of ICoNIoT researchers Igor Moraes and Diogo Mattos, both from UFF.

Learn more about the proposals:

GreenRAN: Sustainable Open RAN for Smart Agriculture and Campuses

The proposal “GreenRAN: Sustainable Open RAN for Smart Agriculture and Campuses‘ offers the ORAN-Green Solution, which will be systematically and extensively evaluated based on two applications developed in different use cases, in the verticals ’Agro 4.0 and Rural Connectivity‘ (primary) and ’Smart Cities and Campuses” (secondary). These applications will use 5G connectivity provided by the Green-5G Network, hosted on the UFPA campus in Belém.

OpenHealth5G

OpenHealth5G, a partnership between UFRGS, PUC-RS, Federal University of Health Sciences, and UNISINOS, proposes the development and testing of innovative applications in Digital and Connected Health, exploring the potential of open 5G and Open RAN network technologies. Prioritised use cases include emergency telehealth, which will enable remote care in critical situations with support for connected medical devices and sensors, and immersive medical education, which will use augmented and virtual reality resources to train students and healthcare professionals.

CampusRAN

The objective of this project is to implement a smart campus at UFF for the development and testing of 5G Open RAN applications. Two applications will be developed, Monitoring Dashboard and Gamified Connectivity Application, which involve the following services: indoor location of UEs, video monitoring for campus community safety and environmental control, indoor and outdoor space sensing, and Internet access for the academic community.

National expansion of the testbed

In addition to selecting applications, the call also approved new host institutions that will receive the OpenRAN@Brasil Programme islands. The increase in the number of hosts strengthens the national expansion of the testbed, allowing for greater infrastructure capillarity and expanding access for researchers in the North, Northeast, and South regions of the country. In the Northeast region, UFRN will be awarded the island, also under the coordination of Professor Augusto Neto.

Research infrastructure

The projects approved by OpenRAN@Brasil are aligned with UNICAMP’s goal of developing and implementing research infrastructure, as well as the associated control tools to enable experimentation in Europe and Brazil, in order to promote experimental research at the point of convergence between optical and wireless networks. Also in 2016, UNICAMP developed the FUTEBOL project with UFRGS, UFMG, UFES, UFCE, and European universities. The project’s objective was to develop and implement research infrastructure, as well as the associated control tools to enable experimentation in Europe and Brazil, in order to promote experimental research at the point of convergence between optical and wireless networks.

Open innovation

One of the great potentials of OpenRAN@Brasil is to implement a testbed environment that opens up many possibilities for research, as it is open. Such testbed environments usually have a cost that can deter researchers with fewer resources. The approved applications will explore the potential of the Open RAN ecosystem, which, in addition to experimentation, will enable technological validation and the generation of applied knowledge in different contexts of use, aligned with national demands for innovation and digital transformation.

The programme is run by RNP, CPQD, Inatel and the Eldorado Institute. It encourages collaboration between industry and academia and seeks to promote collaborative development models, without limiting itself to open source. It seeks to meet the demands of service providers and users of private networks, promote the innovation ecosystem through experimentation and demonstration space, promote application scenarios (public and private networks) and support human resources training.