arXiv subjects

Fatemeh Tavakoli

Publications and source records attributed to Fatemeh Tavakoli.

9 recordsLinked to original sources

arXiv2026

On Privacy in Data-Space Tabular Diffusion Models: Influential Factors, Attacker Knowledge, and Metrics

Tabular data plays an important role in many fields and industries, including those with elevated privacy considerations and risks. As such, there is a rising interest in generating high-quality synthetic proxies for real tabular data as a means of reducing privacy risk and proprietary data exposure. With data-space tabular diffusion models (TDMs) demonstrating leading performance in synthesizing such data, understanding and measuring the privacy risks associated with these models is imperative. Leveraging state-of-the-art membership inference attacks for such TDMs in both black- and white-box settings, this work quantifies the impact of training setup, synthesis choices, and attacker knowledge on privacy leakage. Moreover, the results demonstrate that adversaries need not have perfect knowledge of the training setup, identical data distributions, or massive compute resources to construct successful attacks. Finally, the substantial pitfalls associated with heuristic privacy metrics, such as distance-to-closest record, are identified.

cs.LG↗

arXiv2026

Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deployment

Federated learning (FL) has emerged as a key approach for training models across decentralized data, yet benchmarking in FL remains difficult to reproduce, compare, and extend. Existing evaluations are often tied to custom infrastructure, released as incomplete research code, and conducted primarily in simulation, which limits portability and practical relevance. We present Flower Hub, a platform for publishing, discovering, and executing decentralized and federated applications. We show how it enables reproducible benchmarking by packaging benchmarks as executable, versioned applications with standardized metadata, pinned dependencies, and explicit evaluation workflows. We instantiate this approach with a multi-domain benchmark suite spanning cross-silo and cross-device settings, and including tasks in medical imaging, financial tabular learning, legal instruction tuning, phishing URL detection, and audio tagging. We further demonstrate that the same benchmarking application can run across both simulation and deployment runtimes without changing the application code, enabling unified evaluation across varying learning environments. Beyond model quality, our benchmark design supports system-aware reporting, including runtime and communication metrics. This work advances benchmarking in FL settings from ad hoc code artifacts towards portable, executable, and reusable benchmark applications.

cs.LG↗

arXiv2026

MEMOA: Massive Mixtures of Online Agents via Mean-Field Decentralized Nash Equilibria

In the modern age of large-scale AI, federated learning has become an increasingly important tool for training large populations of AI agents; however, its computational and communication costs can rapidly fail to scale with the number of agents. This is precisely where decentralized agentic strategies shine: each agent acts autonomously, using only its own state together with a minimal summary of the ensemble, namely the mean-field. We derive the unique optimal decentralized policy in closed form. Optimality is characterized through a worst-client/minimax criterion: minimizing the under-performer regret, namely the maximal online cost incurred by the weakest agent in the ensemble. We further prove that the resulting decentralized policy asymptotically converges, in the large-population limit, to the Nash-optimal centralized policy, whose direct computation is not scalable. We use an online weighting mechanism to optimize the server-computed mixture of client predictions, thereby improving the mean prediction in addition to the previously optimized weakest-client prediction. Numerical experiments verify our theoretical guarantees and demonstrate that our decentralized policy typically outperforms natural greedy decentralized baselines.

cs.LG↗

arXiv2025

Adaptive Latent-Space Constraints in Personalized Federated Learning

Federated learning (FL) is an effective and widely used approach to training deep learning models on decentralized datasets held by distinct clients. FL also strengthens both security and privacy protections for training data. Common challenges associated with statistical heterogeneity between distributed datasets have spurred significant interest in personalized FL (pFL) methods, where models combine aspects of global learning with local modeling specific to each client's unique characteristics. This work investigates the efficacy of theoretically supported, adaptive MMD measures in pFL, primarily focusing on the Ditto framework, a state-of-the-art technique for distributed data heterogeneity. The use of such measures significantly improves model performance across a variety of tasks, especially those with pronounced feature heterogeneity. Additional experiments demonstrate that such measures are directly applicable to other pFL techniques and yield similar improvements across a number of datasets. Finally, the results motivate the use of constraints tailored to the various kinds of heterogeneity expected in FL systems.

cs.LG↗

arXiv2025

Automated Capability Evaluation of Foundation Models

Current evaluation frameworks for foundation models rely heavily on static, manually curated benchmarks, limiting their ability to capture the full breadth of model capabilities. This paper introduces Active learning for Capability Evaluation (ACE), a novel framework for scalable, automated, and fine-grained evaluation of foundation models. ACE leverages the knowledge embedded in powerful frontier models to decompose a domain into semantically meaningful capabilities and generates diverse evaluation tasks, significantly reducing human effort. In Mathematics, ACE generated 433 capabilities and 11,800 tasks, covering 94% of Wikipedia-defined skills in the domain while introducing novel, coherent ones. To maximize efficiency, ACE fits a capability model in latent semantic space, allowing reliable approximation of a subject model's performance by evaluating only a subset of capabilities via active learning. It reaches within 0.01 RMSE of exhaustive evaluation by evaluating less than half of capabilities. Compared to static datasets, ACE provides more balanced coverage and uncovers fine-grained differences that aggregate metrics fail to capture. Our results demonstrate that ACE provides a more complete and informative picture of model capabilities, which is essential for safe and well-informed deployment of foundation models.

cs.LG↗

arXiv2025

Online Federation For Mixtures of Proprietary Agents with Black-Box Encoders

Most industry-standard generative AIs and feature encoders are proprietary, offering only black-box access: their outputs are observable, but their internal parameters and architectures remain hidden from the end-user. This black-box access is especially limiting when constructing mixture-of-expert type ensemble models since the user cannot optimize each proprietary AI's internal parameters. Our problem naturally lends itself to a non-competitive game-theoretic lens where each proprietary AI (agent) is inherently competing against the other AI agents, with this competition arising naturally due to their obliviousness of the AI's to their internal structure. In contrast, the user acts as a central planner trying to synchronize the ensemble of competing AIs. We show the existence of the unique Nash equilibrium in the online setting, which we even compute in closed-form by eliciting a feedback mechanism between any given time series and the sequence generated by each (proprietary) AI agent. Our solution is implemented as a decentralized, federated-learning algorithm in which each agent optimizes their structure locally on their machine without ever releasing any internal structure to the others. We obtain refined expressions for pre-trained models such as transformers, random feature models, and echo-state networks. Our ``proprietary federated learning'' algorithm is implemented on a range of real-world and synthetic time-series benchmarks. It achieves orders-of-magnitude improvements in predictive accuracy over natural benchmarks, of which there are surprisingly few due to this natural problem still being largely unexplored.

cs.LG↗

arXiv2024

A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical Datasets

Federated learning (FL) is increasingly being recognized as a key approach to overcoming the data silos that so frequently obstruct the training and deployment of machine-learning models in clinical settings. This work contributes to a growing body of FL research specifically focused on clinical applications along three important directions. First, we expand the FLamby benchmark (du Terrail et al., 2022a) to include a comprehensive evaluation of personalized FL methods and demonstrate substantive performance improvements over the original results. Next, we advocate for a comprehensive checkpointing and evaluation framework for FL to reflect practical settings and provide multiple comparison baselines. To this end, an open-source library aimed at making FL experimentation simpler and more reproducible is released. Finally, we propose an important ablation of PerFCL (Zhang et al., 2022). This ablation results in a natural extension of FENDA (Kim et al., 2016) to the FL setting. Experiments conducted on the FLamby benchmark and GEMINI datasets (Verma et al., 2017) show that the proposed approach is robust to heterogeneous clinical data and often outperforms existing global and personalized FL techniques, including PerFCL.

cs.LG↗

arXiv2024

Dual-ratio approach to pulse oximetry and the effect of skin tone

Significance: Pulsatile blood Oxygen Saturation (SpO2 ) via pulse oximetry is a valuable clinical metric for assessing oxygen delivery. Individual anatomical features, including skin tone, may affect current optical pulse oximetry methods. Aim: Develop an optical pulse oximetry method based on Dual-Ratio (DR) measurements to suppress individual anatomical features on SpO2. Approach: Design a DR-based finger pulse oximeter, hypothesizing that DR would suppress confounds from optical coupling and superficial tissue-absorption. This method is tested using Monte Carlo (MC) simulations and in vivo experiments. Results: Different melanosome volume fraction in the epidermis, a surrogate for skin tone, cause changes in the recovered SpO2 on the order of 1%. Different heterogeneous pulsatile hemodynamics cause greater changes on the order of 10%. SpO2 recovered with DR measurements showed less variability than the traditional Single-Distance (SD) transmission method. Conclusions: For the models and methods considered here, SpO2 measurements are more strongly impacted by heterogeneous pulsatile hemodynamics than by melanosome volume fraction. This is consistent with previous reports that, the skin tone bias is smaller than the observed variation in recovered SpO 2 across individual people. The partial suppression of variability in the SpO2 recovered by DR suggests promise of DR for pulse oximetry.

physics.med-ph↗

arXiv2024

DAEDALUS: Defense Against Firmware ROP Exploits Using Stochastic Software Diversity

This paper presents DAEDALUS, a software diversity-based framework designed to resist ROP attacks on Linux-based IoT devices. DAEDALUS generates unique, semantically equivalent but syntactically different rewrites of IoT firmware, disrupting large-scale replication of ROP attacks. DAEDALUS employs STOKE, a stochastic optimizer for x86 binaries, as its core diversity engine but introduces significant extensions to address unique IoT firmware challenges. DAEDALUS's effectiveness is evaluated using DDoSim, a published botnet DDoS attack simulation testbed. Results demonstrate that DAEDALUS successfully neutralizes ROP payloads by diversifying critical basic blocks in the firmware, preventing attackers from compromising multiple devices for DDoS attacks via memory error vulnerabilities. The findings indicate that DAEDALUS not only mitigates the impact of ROP attacks on individual IoT devices through probabilistic protection but also thwarts large-scale ROP attacks across multiple devices.

cs.CR↗