A talk at MBZUAI discussed federated learning, a distributed machine learning approach training models over devices while keeping data localized. The presentation covered a straggler-resilient federated learning scheme using adaptive node participation to tackle system heterogeneity. It also presented a robust optimization formulation for addressing data heterogeneity and a new algorithm for personalizing learned models. Why it matters: Federated learning is crucial for AI applications involving decentralized data sources, and research on improving its robustness and personalization is essential for real-world deployment in the region.
MBZUAI hosted a panel discussion in collaboration with the Manara Center for Coexistence and Dialogue. Chaoyang He, co-founder of FedML, presented on federated learning (FL), covering privacy/security, resource constraints, label scarcity, and scalable system design. FedML is a platform for zero-code, cross-platform, secure federated learning across industries like healthcare and finance. Why it matters: Federated learning is an important subfield for the GCC region, allowing privacy-preserving model training across distributed data sources.
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.
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 '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.