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The presentation will be titled ‘The Computing Continuum: Beyond Cloud and Edge Intelligence’

With the combination of Internet of things, edge, and cloud computing, computing services can be scattered over a set of computing resources that encompass everything between users’ devices and, including intermediate computing infrastructure deployed in between. The evolving networking technologies promote enhanced bandwidth and data transmission capacity with lower delays, which enables distributed computing resources to be faced as an entangled, distributed heterogeneous platform. This continuum of computing capacity can be used to process large amounts of data with reduced response times. However, creating a seamless distributed computing infrastructure and managing its resources to optimize applications with widely heterogeneous requirements is still a challenge, even after decades of research. The rise of distributed machine learning techniques adds more complexity but also brings additional mechanisms to address this problem. In this talk, I will present an overview of the resource allocation problem, focusing on aspects that can help build an Intelligent Computing Continuum.

The speaker

Luiz Bittencourt is an Associate Professor at Universidade Estadual de Campinas (UNICAMP), Brazil. Luiz was awarded with the IEEE ComSoc Latin America Young Professional Award in 2013. He acts on the organization of several conferences in the cloud and edge computing topics, and in several technical program committees. He served as associate editor for the IEEE Cloud Computing Magazine, and currently serves as AE for the Computers and Electrical Engineering and the Internet of Things journals, for the Journal of Network and Systems Management, and for IEEE Networking Letters. His main interests are in resource management and scheduling in cloud, edge, and fog computing, and their synergy towards an intelligent computing continuum through distributed machine learning techniques.

The 2026 edition of CSBC will feature 10 main events and 16 satellite events, covering different fields and levels of education.

Highlights include the Computer Science Update Conference (JAI), Women in Information Technology (WIT), the Thesis and Dissertation Competition (CTD), the National Computing Conference of Federal Institutes (ENCompIF), and COMPUTEC.

This diversity allows for the participation of a wide range of audiences, from students in their early stages of education to researchers and professionals engaged in advanced discussions on technology, the market, and management.

CSBC 2026 will be held July 19–23 in Gramado. Learn more

The 44th Brazilian Symposium on Computer Networks and Distributed Systems (SBRC 2026) was held in Praia do Forte, Bahia, from May 25 to 29, 2026. On the 27th, our INCT ICoNIoT workshop took place.

The meeting, led by Eduardo Cerqueira (UFPA), featured keynote speaker Torsten Braun from the University of Bern, as well as ICoNIoT researchers Carlos Kamienski (UFABC), Flavia Delicato (UFF), Marcelo Fernandes (UFRN), Allan Souza (UNICAMP), Everton Cavalcante (UFRN), Luiz Fernando Bittencourt (UNICAMP), Edmundo Madeira (UNICAMP), Rafael Lopes (UECE), Augusto Neto (UFRN), and Carlos Trujillo (UNICAMP).

During the event, progress on the projects was presented, and new opportunities for collaboration were opened up.

It was a highly successful moment for our INCT. Check out the video!

 

The 44th Brazilian Symposium on Computer Networks and Distributed Systems (SBRC 2026) was held in Praia do Forte, Bahia, from
May 25 to 29, 2026. In addition to the great success of our workshop, we are also celebrating several achievements by our team:

. The paper “Agent VAMOS! Semantic Context-Aware Vehicle Route Planning with LLM Agents” by Carnot Braun, Daniel Ludovico Guidoni, Eduardo Coelho Cerqueira, Joahannes Bruno Dias da Costa, Leandro Villas, and Allan Mariano de Souza received an Honorable Mention in the Main Track of SBRC 2026.

. The doctoral thesis titled “Overload Management Techniques
and Resource Allocation for Machine-Type Mass Communication in 3GPP Access Networks” by Tiago Pedroso (UNICAMP), and supervised by Prof. Nelson Fonseca (UNICAMP, and general coordinator of ICoNIoT), received an Honorable Mention in the Thesis and Dissertation Contest (CTD) at SBRC 2026.

. The master’s thesis titled “Collision Detection and
Prioritization in Intelligent mMTC Random Access in Cellular
IoT Networks”, authored by Giancarlo Maldonado Cardenas and supervised by professors Nelson L.S. da Fonseca and Carlos A. Astudillo, was awarded
an honorable mention in the Thesis and Dissertation Contest (CTD) at SBRC 2026.

. The paper “Safe Control and Collision Avoidance in Dense Drone Traffic
via Reinforcement Learning,” by Henrique J. Felisardo dos
Santos, Israel da Silva Barros, Luiz Fernando Bittencourt, Carlos
Kamienski, and Fabíola M. C. de Oliveira, received an honorable mention at the Urban Computing Workshop (CoUrb).

. The paper “Self-Supervised Learning for Early Preamble Collision
Detection in Cellular IoT Networks”, authored by Daniela M.
Casas-Velasco, Diogo Maciel Cunha, Marco Aurelio Guerra Pedroso,
Giancarlo Maldonado Cardenas, Carlos Alberto Astudillo Trujillo, and Nelson Fonseca, received an honorable mention at the 1st Workshop on Artificial Intelligence for Computer Networks (WIARC) at SBRC 2026.

. The paper “A Performance Comparison of Authentication and Authorization Patterns for Microservices Applications,” by Rafael Freitas Cardoso (UFRGS) and Jeferson Campos Nobre (UFRGS), received an honorable mention at the WGRS – 31st Workshop on Network and Service Management and Operation at SBRC 2026.

The K-alibra Agent is a Language Model (LM)-based orchestrator designed to dynamically adjust the number of participating clients in each round of Federated Learning (FL). It was created to overcome the rigidity of traditional static algorithms, which typically use a fixed number of clients, a practice that can lead to inefficiency or network overload.

The project was developed through a partnership between researchers from the State University of Campinas (UNICAMP), the Federal University of Pará (UFPA), and the Federal University of Minas Gerais (UFMG), all of which are part of ICoNIoT. They are: Rafael O. Jarczewski (UNICAMP), Eduardo Cerqueira (UFPA), Antonio Loureiro (UFMG), Leandro A. Villas (UNICAMP), and Allan de Souza (UNICAMP). It is published in the proceedings of SBRC 2026 and can be read in full here.

Federated Learning (FL) is a way to train artificial intelligence securely, since each person’s data remains protected on their own devices, without needing to be sent to a central server. The problem is that this process tends to use a lot of data and becomes difficult to manage when there are many users.

Currently, to address this issue, systems select which devices will participate in the training. However, these systems are “stubborn”: they tend to always use the same number of devices, without adjusting that number based on current needs. This causes the process to waste resources or take longer to learn.

To overcome this rigidity, K-Agent was created. It functions as an intelligent “boss” (using language models similar to those behind AI chatbots) that dynamically decides how many devices should participate in each stage.

It works in three steps:

1. It assesses the current state of the training.
2. It reasons to find the best strategy.
3. It acts by determining the optimal number of participants.

Tests have shown that K-Agent is highly efficient: it can reduce internet usage by between 44.4% and 59% compared to traditional methods, while maintaining stable, high-quality learning. Additionally, it can explain the reasoning behind its decisions, making the system more transparent for developers.

The project has been published in the proceedings of SBRC 2026 and can be read in full here.

 

The project is the result of a collaboration among ICoNIoT researchers from four Brazilian universities and is among the nominees for the Best Paper Award at SBRC 2026

VAMOS! is the result of a collaboration between researchers Carnot Braun (State University of Campinas – UNICAMP), Daniel L. Guidoni (Federal University of Ouro Preto – UFOP), Eduardo Cerqueira (Federal University of Pará – UFPA), Joahannes B. D. da Costa (Federal University of São Paulo – UNIFESP), Leandro Villas (UNICAMP), and Allan M. Souza (UNICAMP).

The agent developed by the team, titled VAMOS (Vehicular Agent for Multi-Objective Optimization and Semantics), functions as an intelligent system capable of formulating personalized routes by interpreting the environmental context and the individual priorities of each user. It outperforms traditional navigation systems, which prioritize metric efficiency, such as time and distance, but fail to interpret more complex and context-dependent human intentions.

Unlike conventional navigation systems, this agent uses a Large Language Model (LLM) to suggest strategic stops, such as gas stations or grocery stores, based on continuous learning about the traveler’s profile. The technology’s unique feature lies in its ability to process complex information to optimize routes without the need for overly specific geographic commands.

The project faces the technical challenge of balancing robust processing on external servers with the feasibility of running smaller models directly on mobile devices.

Read the researchers’ paper published in the proceedings of SBRC 2026

The presentation of the SBRC Outstanding Achievement Award—Prof. Otto Carlos Muniz Bandeira Duarte—will take place during the Opening Ceremony of SBRC 2026, to be held on May 26, 2026, in Praia do Forte, Bahia.

Recognition

Prof. Edmundo’s name was chosen by a Selection Committee composed of the recipients of the same award over the past five years, in recognition of his entire career and contributions to the Brazilian scientific community in the fields of Computer Networks and Distributed Systems.

About the Award

The SBRC Outstanding Achievement Award was created in 2012 as part of the celebrations marking the SBRC’s 30th anniversary, and aims to honor members of the SBRC community who have distinguished themselves throughout their lives for their scientific contributions in the fields of computer networks and distributed systems, for their involvement in SBRC activities, and/or for services rendered to the benefit of the Brazilian computer networks and distributed systems community.

In 2022, the Outstanding Achievement Award was renamed the SBRC Prof. Otto Carlos Muniz Bandeira Duarte Outstanding Achievement Award. Otto held a bachelor’s degree in Electronic Engineering from UFRJ, a master’s degree in Electrical Engineering from Coppe/UFRJ, and a doctorate in Teleinformatics from the Ecole Nationale Supérieure des Télécommunications (ENST) in Paris, France. He became a Full Professor at UFRJ in 2003. He was recognized as a CNPq Level 1A Productivity Fellow and as a Scientist of Our State of Rio de Janeiro. He advised 17 doctoral theses, 55 master’s dissertations, and more than 180 undergraduate research projects. He had more than 350 articles published in peer-reviewed journals and conference proceedings.

Career of this Year’s Honoree

Prof. Edmundo is currently a Full Professor at the Institute of Computing at UNICAMP, the university where he earned his Ph.D. in Electrical Engineering in 1991. He has an extensive scientific output in the fields of Computer Networks and Distributed Systems, with over 6,000 citations, covering critical topics such as network management, cloud computing, and network virtualization. His academic reputation was recognized by UNICAMP’s Zeferino Vaz Award for Academic Recognition in 2004, and his work also includes coordinating research projects of national significance and collaborating with various research networks across the country, such as the National Institutes of Science and Technology (INCTs).

Prof. Edmundo has played a prominent role in organizing and structuring the SBRC, the leading event in the field in Brazil, having directly contributed to the technical and scientific quality of the Symposium over the course of several editions. In teaching, he contributes significantly to the training of high-level professionals and the mentoring of young researchers. In addition to teaching, he is actively involved in the scientific community, serving as a member of the Editorial Board of the Journal of Network and Systems Management (JNSM), published by Springer.

 

 

The ICoNIoT workshop at SBRC will take place on May 27 and will feature a special keynote lecture by Torsten Braun, beginning at 10:30 a.m. Please see the table below for the scheduled times for each research theme. The schedule is subject to change until the date of the conference.

Time	Thematic Line
10:15 – 10:30	Opening
10:30 – 11:00	Keynote “Energy-efficient Federated Transfer Learning for Privacy-Preserving Energy-Usage Forecasting”
11:00 – 11:15	IoT
11:15 – 11:30	Digital Health
11:30 – 11:45	5G/6G Networks
12:00 – 14:00	Lunch Break
14:00 – 14:15	Smart Cities
14:15 – 14:30	Edge Computing
14:30 – 14:45	Security
14:45 – 15:00	Vehicular Networks
15:00 – 15:15	Optical Networks
15:15 – 15:20	Closing

The goal is to improve cybersecurity solutions for cloud computing

Researcher Jéferson Nobre (UFRGS) has been working on a relatively new research approach related to cybersecurity applied to cloud computing: confidential computing. As the researcher explains, even though much is known about the field of cybersecurity, there are several challenges that are specific to cloud computing. These challenges require looking beyond what is normally considered in security.

What’s new?

Cloud computing has become the invisible backbone of our digital lives. Text messages, artificial intelligence systems, apps—practically everything depends on this infrastructure. In this context, what are the main security gaps, and where are we most vulnerable?
Today, our devices—especially smartphones—lack the capacity to process everything locally. That’s why we constantly send data to the cloud, where it is processed and returned. And this creates vulnerability, as it’s not uncommon for cloud providers to leak information—even if unintentionally. This is an attack on confidentiality and privacy, which has been observed in many incidents in recent years, fueling growing concern about this vulnerability.

In information security, two fundamental concepts are confidentiality and privacy. Confidentiality is the responsibility of organizations or companies that are accountable for user privacy. That is, service providers must ensure that only they and the individuals to whom they grant access rights will access user information. Users, on the other hand, have the right to keep their information private.

The greatest vulnerability, therefore, lies in processing. Although there are mature solutions for data at rest and in transit, the processing phase remains a gap that confidential computing seeks to fill in order to extend security guarantees to the moment of processing.

The contribution of Confidential Computing

In this context, confidential computing aims to provide a set of techniques and architectures that enable workloads to be executed in isolated environments, with formal guarantees of confidentiality and integrity.
Confidential computing is based on the idea that it is possible to create, within the cloud, a secure environment in which data can be processed without compromising confidentiality. This is made possible through so-called Trusted Execution Environments (TEEs). This technology is hardware-based and works by creating, within the processor itself, an isolated and protected area. In this space, both the data and the code remain encrypted, preventing external access—including by the cloud provider.
In addition, this environment supports a mechanism called remote attestation, which allows for remote verification that the code being executed is exactly the one that was originally submitted and that it is running within a secure environment. This increases confidence in the processing of sensitive data in the cloud.

Anonymous Communication

The problem is that, even with these approaches, it is still possible to identify who generated a particular workload by analyzing the traffic. This type of vulnerability is associated with so-called metadata attacks—that is, information about the data itself, such as who sent it, the volume transmitted, the time, and the frequency of interactions. To mitigate this risk, anonymous communication has emerged, whose purpose is to decouple the data from the identity of the sender by separating this information.

Currently, there are already some standards in this area. One of the main ones is OHTTP (Oblivious HTTP), a variation of the HTTP protocol that introduces anonymity into data transmission. This model requires the presence of an intermediary element independent of the organization (relay resource) and a gateway, acting between the user and the trusted execution environment. This adds an extra layer of protection, making it difficult to correlate the transmitted data with its source.

The case of messaging systems

As a central case study, Nobre highlights the Meta Private Processing system, which uses Trusted Execution Environments (TEEs), remote attestation, and the Oblivious HTTP protocol to process WhatsApp messages via the cloud (the only possible option, since it is not possible to process the information using AI with the resources of each user’s smartphone) without the company accessing the content or metadata.
The idea is to generate conversation summaries using AI without the provider having access to the content, which would guarantee user privacy (currently ensured by end-to-end encryption).
The solution would be a confidential computing + anonymous computing pipeline, enabling AI processing in a way that preserves the privacy promises WhatsApp makes. In this solution, no component has simultaneous access to the user’s identity, the content, and the execution environment.

Nowadays, Meta has Meta AI, which is manually added to a conversation and has access only to what the user explicitly sends, not to the user’s entire inbox. This is a superficial control. In the case of Private Processing, the user’s entire inbox is processed, and the user must voluntarily enable this feature. Confidentiality is ensured by a set of technologies that include TEE and OHTTP. An intermediary company is responsible for decoupling the source and destination, and another company handles the audit—which presents a challenge, as this company must be independent and reputable. Additionally, another organization plays a role in configuring cryptographic keys. For the adoption of these technologies, ecosystem fragmentation is one of the major obstacles.

Additional challenge

The encrypted environment within the cloud comes at a high cost. The use of TEEs requires that the Large Language Model (LLM) be run entirely within this secure environment. In this context, it is not appropriate to use general models belonging to service providers, such as those from Meta, since this could imply the use of processed data for training purposes. Thus, processing must occur in isolation within the trusted environment, ensuring that the data is used exclusively for task execution and subsequently deleted without any retention.

How to address confidential computing and anonymous communication

Confidential computing is capable of bridging the gap between data protection at rest/in transit and in use, which represents a real advance but does not constitute a complete solution for cloud system security. The guarantees depend on the integrity of the hardware, firmware, supply chain, and attestation services. Trust is extended and redistributed. In this context, anonymous communication is complementary, protecting metadata that anonymous computing alone does not cover. Auditability and transparency are non-optional requirements, as independent audits and immutable logs are part of the trust model.

Watch Jéferson Nobre’s presentation

On April 16, 2026, researcher Jéferson Nobre presented a webinar titled “Security Analysis of Confidential Computing and Anonymous Communication,” providing examples and diagrams to help illustrate the topics discussed. Watch it on our YouTube channel

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.