This paper introduces DaringFed, a novel dynamic Bayesian persuasion pricing mechanism for online federated learning (OFL) that addresses the challenge of two-sided incomplete information (TII) regarding resources. It formulates the interaction between the server and clients as a dynamic signaling and pricing allocation problem within a Bayesian persuasion game, demonstrating the existence of a unique Bayesian persuasion Nash equilibrium. Evaluations on real and synthetic datasets demonstrate that DaringFed optimizes accuracy and convergence speed and improves the server's utility.
MBZUAI researchers have developed a new method called "Byzantine antidote" (Bant) to defend federated learning systems against Byzantine attacks, where malicious nodes intentionally disrupt the training process. Bant uses trust scores and a trial function to dynamically filter out corrupted updates, even when most nodes are compromised. The system can identify poorly labeled data while still training models effectively, addressing both unconscious mistakes and deliberate sabotage. Why it matters: This research enhances the reliability and security of federated learning in sensitive sectors like healthcare and finance, enabling safer collaborative AI development.
MBZUAI researchers developed FeSViBS, a new federated split learning technique for vision transformers that addresses data scarcity and privacy concerns in healthcare image classification. The method combines federated learning and split learning to train models collaboratively without sharing sensitive patient data directly. It overcomes limitations of traditional centralized training and vulnerabilities in federated learning. Why it matters: This approach enables the development of AI-powered healthcare applications while adhering to stringent data privacy regulations, unlocking the potential of machine learning in medical imaging.
Researchers from MBZUAI, KAUST, and Mila are collaborating to develop methods for identifying and mitigating the impact of malicious actors in federated learning systems used for health data analysis. These systems aggregate anonymized data from numerous devices to generate insights for healthcare improvements. The team's research, accepted at ICLR 2023, focuses on using variance reduction techniques to counteract the disruptive effects of skewed or corrupted data submitted by dishonest users. Why it matters: Protecting the integrity of AI-driven health systems is crucial for ensuring the reliability and safety of insights derived from sensitive patient data in the GCC region and globally.
MBZUAI researchers have developed 'Byzantine antidote' (Bant), a novel defense mechanism against Byzantine attacks in federated learning. Bant uses trust scores and a trial function to dynamically filter and neutralize corrupted updates, even when a majority of nodes are compromised. The research was presented at the 40th Annual AAAI Conference on Artificial Intelligence.
Professor Mérouane Debbah, Chief Researcher at AIDRC, and his co-authors received the 2022 IEEE TAOS TC Best GCSN Paper Award for their work on federated quantized neural networks. The paper, presented at IEEE ICC 2022, explores the tradeoff between energy, precision, and accuracy in these networks. The research proposes an optimal quantization level to minimize energy consumption during training, making it less prohibitive for mobile devices. Why it matters: The award recognizes work that reduces the carbon footprint of large-scale AI systems, a key challenge for sustainable AI deployment in the region and globally.
KAUST Ph.D. student Mohammed Aljahdali received the Best Paper award at the International Conference on Federated Learning Technologies and Applications (FLTA) 2025 for his research on federated learning. His paper, "Flashback: Understanding and Mitigating Forgetting in Federated Learning," introduces an algorithm to help AI systems retain knowledge across diverse datasets while preserving privacy. Aljahdali's research, supervised by Professor Marco Canini, focuses on training machine learning models directly on user devices. Why it matters: This award recognizes the growing talent and impactful research emerging from Saudi universities in the field of privacy-preserving AI.
KAUST is hosting a workshop on distributed training in November 2025, led by Professors Peter Richtarik and Marco Canini, focusing on scaling large models like LLMs and ViTs. Richtarik's team recently solved a 75-year-old problem in asynchronous optimization, developing time-optimal stochastic gradient descent algorithms. This research improves the speed and reliability of large model training and supports applications in distributed and federated learning. Why it matters: KAUST's focus on scalable AI and federated learning contributes to Saudi Arabia's Vision 2030 goals and addresses critical challenges in AI deployment and data privacy.