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1. Alternative Robust Design Formulation and Solution Space Exploration Strategies to Manage Uncertainty in the Design of Engineering Systems

   https://doi.org/10.1115/DETC2025-169771  

   Mukundan, Pooja; Digonta, H_M_Dilshad Alam; Baby, Mathew; Nellippallil, Anand Balu (August 2025, American Society of Mechanical Engineers)

   Abstract The design of complex engineering systems often relies on simulation-based models, which inherently introduce uncertainty. Consequently, effective system design necessitates the incorporation of robust design principles and metrics to manage the uncertainties. From a decision-based design perspective, metrics such as the Design Capability Index (DCI) and Error Margin Index (EMI) are employed in problem formulations to address this challenge. These metrics typically utilize Taylor series-based approximations to estimate variance. However, due to the structural limitations of the Taylor series expansion, these formulations may mislead designers toward local minima rather than guide them toward actual robust solutions. In this paper, we propose an alternative formulation of the Design Capability Index (DCI) and Error Margin Index (EMI) that leverages the multiple-point variance estimation method. This approach employs a weighted average of multiple points to assess variance estimations around the nominal design, ensuring that the solution is not confined to a local optimum by estimating the correct variance at the local optimum. The proposed alternative metrics are incorporated into the robust Concept Exploration Framework (rCEF) to find robust solutions for complex engineering systems. A machine learning-based visualization tool, the interpretable Self-Organizing Maps (iSOM), is used to visualize and explore the high-dimensional robust solution space generated using the updated rCEF. Two examples are used to evaluate the proposed formulation: (i) a polynomial function to examine the effect of the alternative variance estimation method on EMI and DCI metrics, and (ii) a comprehensive hot rod rolling problem, considering model parameters and model structure uncertainty. 

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2. FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update

   https://doi.org/10.1609/aaai.v38i12.29297  

   Liu, Ji; Jia, Juncheng; Che, Tianshi; Huo, Chao; Ren, Jiaxiang; Zhou, Yang; Dai, Huaiyu; Dou, Dejing (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   As a promising approach to deal with distributed data, Federated Learning (FL) achieves major advancements in recent years. FL enables collaborative model training by exploiting the raw data dispersed in multiple edge devices. However, the data is generally non-independent and identically distributed, i.e., statistical heterogeneity, and the edge devices significantly differ in terms of both computation and communication capacity, i.e., system heterogeneity. The statistical heterogeneity leads to severe accuracy degradation while the system heterogeneity significantly prolongs the training process. In order to address the heterogeneity issue, we propose an Asynchronous Staleness-aware Model Update FL framework, i.e., FedASMU, with two novel methods. First, we propose an asynchronous FL system model with a dynamical model aggregation method between updated local models and the global model on the server for superior accuracy and high efficiency. Then, we propose an adaptive local model adjustment method by aggregating the fresh global model with local models on devices to further improve the accuracy. Extensive experimentation with 6 models and 5 public datasets demonstrates that FedASMU significantly outperforms baseline approaches in terms of accuracy (0.60% to 23.90% higher) and efficiency (3.54% to 97.98% faster). 

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3. Federated learning framework integrating REFINED CNN and Deep Regression Forests

   https://doi.org/10.1093/bioadv/vbad036  

   Nolte, Daniel; Bazgir, Omid; Ghosh, Souparno; Pal, Ranadip (March 2023, Bioinformatics Advances)

   Rattray, Magnus (Ed.)

   Abstract SummaryPredictive learning from medical data incurs additional challenge due to concerns over privacy and security of personal data. Federated learning, intentionally structured to preserve high level of privacy, is emerging to be an attractive way to generate cross-silo predictions in medical scenarios. However, the impact of severe population-level heterogeneity on federated learners is not well explored. In this article, we propose a methodology to detect presence of population heterogeneity in federated settings and propose a solution to handle such heterogeneity by developing a federated version of Deep Regression Forests. Additionally, we demonstrate that the recently conceptualized REpresentation of Features as Images with NEighborhood Dependencies CNN framework can be combined with the proposed Federated Deep Regression Forests to provide improved performance as compared to existing approaches. Availability and implementationThe Python source code for reproducing the main results are available on GitHub: https://github.com/DanielNolte/FederatedDeepRegressionForests. Contactranadip.pal@ttu.edu Supplementary informationSupplementary data are available at Bioinformatics Advances online. 

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4. Federated Graph Classification over Non-IID Graphs

   Xie, Han; Ma, Jing; Xiong, Li; Yang, Carl (January 2021, Advances in neural information processing systems)

   Federated learning has emerged as an important paradigm for training machine learning models in different domains. For graph-level tasks such as graph classification, graphs can also be regarded as a special type of data samples, which can be collected and stored in separate local systems. Similar to other domains, multiple local systems, each holding a small set of graphs, may benefit from collaboratively training a powerful graph mining model, such as the popular graph neural networks (GNNs). To provide more motivation towards such endeavors, we analyze real-world graphs from different domains to confirm that they indeed share certain graph properties that are statistically significant compared with random graphs. However, we also find that different sets of graphs, even from the same domain or same dataset, are non-IID regarding both graph structures and node features. To handle this, we propose a graph clustered federated learning (GCFL) framework that dynamically finds clusters of local systems based on the gradients of GNNs, and theoretically justify that such clusters can reduce the structure and feature heterogeneity among graphs owned by the local systems. Moreover, we observe the gradients of GNNs to be rather fluctuating in GCFL which impedes high-quality clustering, and design a gradient sequence-based clustering mechanism based on dynamic time warping (GCFL+). Extensive experimental results and in-depth analysis demonstrate the effectiveness of our proposed frameworks. 

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5. Causal inference with invalid instruments: post-selection problems and a solution using searching and sampling

   https://doi.org/10.1093/jrsssb/qkad049  

   Guo, Zijian (May 2023, Journal of the Royal Statistical Society Series B: Statistical Methodology)

   Abstract Instrumental variable methods are among the most commonly used causal inference approaches to deal with unmeasured confounders in observational studies. The presence of invalid instruments is the primary concern for practical applications, and a fast-growing area of research is inference for the causal effect with possibly invalid instruments. This paper illustrates that the existing confidence intervals may undercover when the valid and invalid instruments are hard to separate in a data-dependent way. To address this, we construct uniformly valid confidence intervals that are robust to the mistakes in separating valid and invalid instruments. We propose to search for a range of treatment effect values that lead to sufficiently many valid instruments. We further devise a novel sampling method, which, together with searching, leads to a more precise confidence interval. Our proposed searching and sampling confidence intervals are uniformly valid and achieve the parametric length under the finite-sample majority and plurality rules. We apply our proposal to examine the effect of education on earnings. The proposed method is implemented in the R package RobustIV available from CRAN. 

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