Advancing Federated Learning in 6G: A Trusted Architecture with Graph-based Analysis

Integrating native AI support into the network architecture is an essential objective of 6G. Federated Learning (FL) emerges as a potential paradigm, facilitating decentralized AI model training across a diverse range of devices under the coordination of a central server. However, several challenges hinder its wide application in the 6G context, such as malicious attacks and privacy snooping on local model updates, and centralization pitfalls. This work proposes a trusted architecture for supporting FL, which utilizes Distributed Ledger Technology (DLT) and Graph Neural Network (GNN), including three key features. First, a pre-processing layer employing homomorphic encryption is incorporated to securely aggregate local models, preserving the privacy of individual models. Second, given the distributed nature and graph structure between clients and nodes in the pre-processing layer, GNN is leveraged to identify abnormal local models, enhancing system security. Third, DLT is utilized to decentralize the system by selecting one of the candidates to perform the central server's functions. Additionally, DLT ensures reliable data management by recording data exchanges in an immutable and transparent ledger. The feasibility of the novel architecture is validated through simulations, demonstrating improved performance in anomalous model detection and global model accuracy compared to relevant baselines.Comment: Accepted by IEEE Global Communications Conference (GLOBECOM) 202

Probabilistically Rewired Message-Passing Neural Networks

Message-passing graph neural networks (MPNNs) emerged as powerful tools for processing graph-structured input. However, they operate on a fixed input graph structure, ignoring potential noise and missing information. Furthermore, their local aggregation mechanism can lead to problems such as over-squashing and limited expressive power in capturing relevant graph structures. Existing solutions to these challenges have primarily relied on heuristic methods, often disregarding the underlying data distribution. Hence, devising principled approaches for learning to infer graph structures relevant to the given prediction task remains an open challenge. In this work, leveraging recent progress in exact and differentiable $k$-subset sampling, we devise probabilistically rewired MPNNs (PR-MPNNs), which learn to add relevant edges while omitting less beneficial ones. For the first time, our theoretical analysis explores how PR-MPNNs enhance expressive power, and we identify precise conditions under which they outperform purely randomized approaches. Empirically, we demonstrate that our approach effectively mitigates issues like over-squashing and under-reaching. In addition, on established real-world datasets, our method exhibits competitive or superior predictive performance compared to traditional MPNN models and recent graph transformer architectures

The Global Success of Mycobacterium tuberculosis Modern Beijing Family Is Driven by a Few Recently Emerged Strains

ABSTRACT Strains of the Mycobacterium tuberculosis complex (MTBC) Beijing family aroused concern because they were often found in clusters and appeared to be exceptionally transmissible. However, it was later found that strains of the Beijing family were heterogeneous, and the transmission advantage was restricted to sublineage L2.3 or modern Beijing. In this study, we analyzed the previously published genome sequences of 7,896 L2.3 strains from 51 different countries. Using BEAST software to approximate the temporal emergence of L2.3, our calculations suggest that L2.3 initially emerged in northern East Asia during the early 15th century and subsequently diverged into six phylogenetic clades, identified as L2.3.1 through L2.3.6. Using terminal branch length and genomic clustering as proxies for transmissibility, we found that the six clades displayed distinct population dynamics, with the three recently emerged clades (L2.3.4 to L2.3.6) exhibiting significantly higher transmissibility than the older three clades (L2.3.1 to L2.3.3). Of the Beijing family strains isolated outside East Asia, 83.1% belonged to the clades L2.3.4 to L2.3.6, which were also associated with more cross-border transmission. This work reveals the heterogeneity in sublineage L2.3 and demonstrates that the global success of Beijing family strains is driven by the three recently emerged L2.3 clades. IMPORTANCE The recent population dynamics of the global tuberculosis epidemic are heavily shaped by Mycobacterium tuberculosis complex (MTBC) strains with enhanced transmissibility. The infamous Beijing family strain stands out because it has rapidly spread throughout the world. Identifying the strains responsible for the global expansion and tracing their evolution should help to understand the nature of high transmissibility and develop effective strategies to control transmission. In this study, we found that the L2.3 sublineage diversified into six phylogenetic clades (L2.3.1 to L2.3.6) with various transmission characteristics. Clades L2.3.4 to L2.3.6 exhibited significantly higher transmissibility than clades L2.3.1 to L2.3.3, which helps explain why more than 80% of Beijing family strains collected outside East Asia belong to these three clades. We conclude that the global success of L2.3 was not caused by the entire L2.3 sublineage but rather was due to the rapid expansion of L2.3.4 to L2.3.6. Tracking the transmission of L2.3.4 to L2.3.6 strains can help to formulate targeted TB prevention and control

Robust production of monovalent bispecific IgG antibodies through novel electrostatic steering mutations at the CH1-Cλ interface

ABSTRACTBispecific antibodies represent an increasingly large fraction of biologics in therapeutic development due to their expanded scope in functional capabilities. Asymmetric monovalent bispecific IgGs (bsIgGs) have the additional advantage of maintaining a native antibody-like structure, which can provide favorable pharmacology and pharmacokinetic profiles. The production of correctly assembled asymmetric monovalent bsIgGs, however, is a complex engineering endeavor due to the propensity for non-cognate heavy and light chains to mis-pair. Previously, we introduced the DuetMab platform as a general solution for the production of bsIgGs, which utilizes an engineered interchain disulfide bond in one of the CH1-CL domains to promote orthogonal chain pairing between heavy and light chains. While highly effective in promoting cognate heavy and light chain pairing, residual chain mispairing could be detected for specific combinations of Fv pairs. Here, we present enhancements to the DuetMab design that improve chain pairing and production through the introduction of novel electrostatic steering mutations at the CH1-CL interface with lambda light chains (CH1-Cλ). These mutations work together with previously established charge-pair mutations at the CH1-CL interface with kappa light chains (CH1-Cκ) and Fab disulfide engineering to promote cognate heavy and light chain pairing and enable the reliable production of bsIgGs. Importantly, these enhanced DuetMabs do not require engineering of the variable domains and are robust when applied to a panel of bsIgGs with diverse Fv sequences. We present a comprehensive biochemical, biophysical, and functional characterization of the resulting DuetMabs to demonstrate compatibility with industrial production benchmarks. Overall, this enhanced DuetMab platform substantially streamlines process development of these disruptive biotherapeutics