The emerging paradigm of 6G wireless communication networks envisions ultra-reliable low-latency communication (URLLC), massive machine-type communications (mMTC), and pervasive edge computing intelligence. In heterogeneous mobile edge computing (MEC) networks, dynamically offloading compute-intensive tasks (e.g., augmented reality rendering, connected vehicular telemetry, autonomous robotic control) while orchestrating multi-tenant network slicing under time-varying channel conditions is an NP-hard stochastic optimization problem. Centralized reinforcement learning algorithms suffer from extreme communication overhead, severe backhaul congestion, and severe privacy vulnerabilities. Conversely, standard synchronous Federated Learning (FL) methods encounter severe 'straggler effects' caused by heterogeneous edge device processing capabilities. In this paper, we propose AF-EdgeRL, a novel Byzantine-resilient Asynchronous Federated Reinforcement Learning framework tailored for distributed resource allocation and dynamic task offloading. AF-EdgeRL deploys a distributed Proximal Policy Optimization (PPO) agent across edge servers and end-user devices, combined with a Staleness-Aware Adaptive Weight Aggregator (SAWA) that dynamically adjusts model update gradients based on hardware compute latency and channel state information (CSI). Furthermore, we establish theoretical convergence guarantees under non-convex reinforcement learning objectives. Evaluated on a high-fidelity 6G MEC simulator with real-world mobile mobility traces (Telecom Italia Milano dataset), AF-EdgeRL reduces end-to-end task execution latency by 41.2%, achieves 99.999% URLLC deadline compliance, and decreases edge energy consumption by 32.6% compared to state-of-the-art synchronous FedRL and centralized DRL baselines.
Daniel Merrow, Tember L. Nair, Lucas Farnandez· International Journal of App...· 0 citations
Executive Overview This consolidated release of Paper X unifies empirical findings, mathematical foundations, and real-world implementation proofs for AuraOS—a local-first, zero-extraction computational architecture designed to eliminate recurring cloud SaaS overhead and API token extraction. By decoupling spatial reconstruction, neural synthesis, and automated video orchestration from centralized cloud infrastructure, this work demonstrates that modern consumer hardware (standard laptops and smartphones) can execute high-throughput generative and spatial tasks deterministically at zero marginal cost. Flagship Public Commons Release: The Aura Creator Studio As part of the Aura Commons commitment to public, unrestricted tooling, this release delivers the Aura Creator Studio—a sovereign, automated video production and spatial intelligence suite engineered specifically for independent video editors, YouTube creators, and TikTok content producers: Monocular 3D Spatial Triangulation & SLAM: Extracts 3D metric floorplans, doorway apertures, and 4D entity trajectories from unstructured 2D gameplay/video captures using dynamic HUD exclusion masking, pointmap regression, and Kalman-RTS smoothing. Dual-Sensor Gaussian Splatting (3DGS): Combines stationary laptop camera anchors with mobile orbital scans to bake persistent surface features (e.g., decals, wall artwork) into 3D Gaussians with zero temporal drift. Procedural Media & Multi-Track Synthesis: Features local neural text-to-speech (Edge-TTS / Piper), animated karaoke typography with Bézier bounding pills, and zero-dependency procedural DSP audio synthesis ($140\text{ Hz} \to 42\text{ Hz}$ sub-bass transients) without stock licensing fees. AirLLM & Council V3 Layer Streaming: Executes 8B to 70B parameter open models locally on standard laptop NVMe drives, providing fact-grounded scriptwriting and low-poly 3D graybox pre-visualization with zero cloud API token billing. Sovereign Gate 10 Governance & Attribution DAG: Guarantees non-delegable human approval before publishing while sealing public commons attribution and microtransaction splits into immutable SHA-256 ledgers. The Macro-Economic Amortization Thesis The primary bottleneck for digital creators is platform extraction—a compounding cycle of recurring monthly subscriptions for voice cloning, video splicing, background removal, 3D rendering, and LLM tokens that drains $50 to $300+ per month per creator. When amortized across a community of 100,000 creators, the AuraOS architecture redirects $60,000,000 to $360,000,000 annually from centralized cloud monopolies back into creator equity. By maximizing the idle compute capacity of hardware creators already own, the marginal cost of end-to-end creative production collapses to zero. Open Scientific Invitation: Challenge, Replicate, and Falsify Science advances through rigorous scrutiny, empirical falsification, and open replication. We openly invite computer vision researchers, systems architects, machine learning engineers, and skeptics to: Audit the Mathematical Formulations: Stress-test the Kalman-RTS trajectory smoothing, coordinate back-projection matrices, and Bézier vector geometry. Replicate the Local Benchmarks: Run the provided scripts and verify that complete video assemblies and spatial reconstructions execute fully offline on consumer-grade hardware. Challenge and Extend the Commons: Benchmark the throughput, test edge cases in unconstrained monocular footage, and submit critical evaluations. All code, pipeline orchestrators, and cryptographic verification receipts are open-source and free for public examination and commercial liberation under the Aura Open Commons (CC-BY-SA-4.0). Version 2.0 Changelog Entry (for Zenodo "Additional Notes") Markdown ### Version 2.0 Update Notes - Consolidated multi-modal spatial tracking proofs and 3D Gaussian Splatting manifests. - Added full architectural specification for the Aura Creator Studio (Public Commons Release 1). - Integrated Council V3 graybox pre-visualization and zero-SaaS AirLLM pipeline benchmarks. - Established open peer challenge and replication guidelines for repository artifacts. Aura is an open cognitive commons: a model-orthogonal operating substrate designed to let anyone build powerful AI systems without locking intelligence, memory, coordination, or computation inside a single model, vendor, device, or company. Paper X publishes the Aura World Seed and the current AuraOS architecture as a defensive technical disclosure and reproducible reference system. Its central inversion is simple: Do not feed the AI the world. Compile the smallest source-resolvable world sufficient for the objective. Aura externalizes persistent cognition into a Coordinate Memory System: source-bound semantic identities, generations, currentness, authority, provenance, relations, residual obligations, and exact reopen paths remain durable, while prompts, models, KV caches, workers, runtimes, devices, and interfaces remain replaceable. A model can therefore wake only the portion of the world capable of changing the current consequence rather than repeatedly reconstructing its entire context. The architecture includes objective-native Ephemeral Arenas: temporary apps, tools, agent teams, simulations, interfaces, and execution environments that assemble around an intent, receive only the capabilities and context they need, produce verifiable receipts, collapse their useful state back into the commons, and dissolve. Aura is designed so applications can be temporary while knowledge, provenance, and continuity persist. Paper X also publishes the mechanisms behind Aura's efficiency claims so others can test, reproduce, challenge, and falsify them: polysynthetic/FST intent compression, minimum-sufficient L0→L4 hydration, semantic coordinates, affected-cone recomputation, HyperDrive normal-form collapse, HyperScale routing, consequence-aware caching, swarm coordination, and Runtime Arenas. The paper reports provider telemetry across 9,381 requests in which 97.4029% of input tokens were served as cache hits, with $17.77 actual provider cost versus $209.58 in a price-only cache-miss counterfactual. This is reported specifically as measured provider reuse—not as a claim that Aura uniquely caused a 97% reduction in logical token volume—and the architecture is presented so independent builders can run stronger matched-control tests. Aura is not intended to be the product. It is infrastructure for products, communities, agents, researchers, creators, enterprises, and sovereign systems to build upon. The AGPL-covered Aura substrate remains part of the commons, while the ecosystem is designed for independent builders to create their own applications, services, Arenas, experiences, and businesses around it subject to the license. Paper X includes the World Seed, compact activation kernels, Coordinate Cache Fabric, Triadic Construct/Challenge/Verify process, recursive swarms, HyperDrive/HyperScale mathematics, Runtime Arena V0.3, host compilation, semantic-spatial interfaces, proof-carrying execution, and a path toward federated planetary coordination without requiring a single globally hot model or context. The goal is straightforward: make intelligence require less context, less computation, less energy, less duplication, and less centralized control — while preserving more provenance, accountability, interoperability, and human agency. Build with it. Test it. Break it. Improve it. The commons gets stronger when everyone can use it.
Dallas Courchene· Zenodo (CERN European Organi...· 0 citations
Reproducibility capsule for FusionNet Lite, a lightweight sequence-fusion deep-learning pipeline for predictive maintenance in industrial IoT systems. The campaign evaluates FusionNet Lite alongside CNN, BiLSTM, MLP, and CNN-LSTM baselines using centralized and FedAvg federated training across five seeds. The capsule includes leakage-safe preprocessing, stratified AI4I splitting, event-aware chronological MetroPT3 splitting with all rows retained, Dirichlet label non-IID client allocation, integrated-gradients analysis, statistical comparisons, publication tables and figures, and a consistency audit. Dataset files are placeholders in this capsule. Full reproducible execution requires attaching the authorised AI4I 2020 and MetroPT3 datasets under /data before selecting full mode.
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL methods often rely on a central server, raising significant privacy concerns, especially when dealing with sensitive data residing on edge devices. This paper proposes a decentralized federated learning framework incorporating differential privacy to mitigate these risks. The core idea is to eliminate the central server and enable collaborative learning directly among edge devices, while simultaneously safeguarding individual data privacy using differential privacy mechanisms. Our approach utilizes a novel decentralized algorithm that leverages local model updates and a privacy-preserving aggregation protocol. We demonstrate the effectiveness of this framework through a theoretical analysis and provide a detailed formulation of the decentralized FL process. The key mathematical formulations related to the algorithm are presented, including the privacy loss budget calculation, local model updates, and the aggregated model update. This work contributes to the development of robust and privacy-preserving FL solutions for edge computing environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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Acute lateral ankle sprain is among the most frequent injuries in sports medicine, and its high recurrence rate and propensity toward chronic ankle instability (CAI) constitute a persistent clinical challenge. Conventional risk assessment—relying on subjective questionnaires, physical examination, and clinical experience—captures only part of the complex biomechanical and neuromuscular adaptations that follow an initial sprain. Recent advances in objective biomechanical profiling and machine learning (ML) have opened a new paradigm for individualized recurrence prediction. This review synthesizes the full translational chain from biomechanical marker identification to ML-based risk prediction. We first summarize key markers spanning gait kinetics (ground reaction forces and joint moments), proprioceptive and neuromuscular control deficits, and dynamic postural stability, highlighting the representational advantages of multimodal data fusion. We then compare mainstream ML architectures—including tree-based ensembles, recurrent networks for gait time series, and strategies for small-sample learning—and discuss the role of explainable artificial intelligence (XAI) in linking predictions to injury mechanisms. Evidence indicates that models integrating multimodal biomechanical features outperform conventional clinical scores (e.g., Ankle-GO, AUC 0.70), with few-shot learning systems achieving test accuracies of 0.89 and inertial-sensor-driven recurrent networks estimating ankle kinematics with coefficients of determination up to 0.93. Finally, we evaluate validation strategies, clinical utility in rehabilitation prescription and return-to-sport decisions, and wearable-based long-term monitoring, and we dissect the outstanding challenges of data standardization, model interpretability, annotation scarcity, and ethical governance. Emerging technologies—digital twins, federated learning, and generative AI for data augmentation—offer plausible routes toward a precise, interpretable, and equitable intelligent prevention ecosystem for recurrent ankle sprain.
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL methods often rely on a central server, raising significant privacy concerns, especially when dealing with sensitive data residing on edge devices. This paper proposes a decentralized federated learning framework incorporating differential privacy to mitigate these risks. The core idea is to eliminate the central server and enable collaborative learning directly among edge devices, while simultaneously safeguarding individual data privacy using differential privacy mechanisms. Our approach utilizes a novel decentralized algorithm that leverages local model updates and a privacy-preserving aggregation protocol. We demonstrate the effectiveness of this framework through a theoretical analysis and provide a detailed formulation of the decentralized FL process. The key mathematical formulations related to the algorithm are presented, including the privacy loss budget calculation, local model updates, and the aggregated model update. This work contributes to the development of robust and privacy-preserving FL solutions for edge computing environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL schemes are vulnerable to privacy attacks, particularly model inversion attacks, which can reveal sensitive information about the underlying data. This paper proposes a novel decentralized federated learning framework that integrates secure aggregation and differential privacy to mitigate these risks. The system utilizes secure aggregation techniques, such as homomorphic encryption, to protect individual model updates during the aggregation process. Simultaneously, differential privacy mechanisms are employed to limit the amount of information leaked about individual clients' data. The decentralized nature of the framework enhances robustness and scalability. This approach significantly strengthens the privacy guarantees of FL while maintaining model accuracy and efficiency. The core claim of this work is that enhancing security and privacy is crucial for the wider adoption of federated learning. The proposed mechanism combines secure aggregation and differential privacy in a decentralized federated learning framework.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Residential load forecasting underpins demand response and local energy trading, yet privacy regulations such as the EU GDPR prevent service providers from collecting fine-grained household consumption data. Federated learning (FL) enables collaborative training without raw data exchange, and client clustering widely mitigates accuracy degradation caused by non-IID residential load distributions. However, existing clustering benchmarks only compare grouped schemes against a single global FL model, ignoring household-specific fine-tuning as a strong personalized baseline. This work constructs a personalization-fair benchmark to quantify the true value of client clustering for residential load forecasting. Six FL strategies are evaluated across three data heterogeneity regimes built on two real-world smart meter datasets. Results show clustering reduces forecasting errors by up to 26% at the cluster-model stage, yet after household fine-tuning all methods land within 2% of the global model and deliver nearly identical performance; adaptive re-clustering brings no accuracy improvement over static grouping. The primary merit of clustering lies in communication efficiency, as clustered frameworks converge within one-third of the communication rounds required by the standard global model under highly heterogeneous PV prosumer portfolios. Finally, this paper releases an open Ausgrid benchmark with a temporal out-of-time evaluation protocol for follow-up federated load forecasting research.
Ran Zheng, Sara Barja-Martínez, Mònica Aragüés‐Peñalba et al.· Energy Reports· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL schemes are vulnerable to privacy attacks, particularly model inversion attacks, which can reveal sensitive information about the underlying data. This paper proposes a novel decentralized federated learning framework that integrates secure aggregation and differential privacy to mitigate these risks. The system utilizes secure aggregation techniques, such as homomorphic encryption, to protect individual model updates during the aggregation process. Simultaneously, differential privacy mechanisms are employed to limit the amount of information leaked about individual clients' data. The decentralized nature of the framework enhances robustness and scalability. This approach significantly strengthens the privacy guarantees of FL while maintaining model accuracy and efficiency. The core claim of this work is that enhancing security and privacy is crucial for the wider adoption of federated learning. The proposed mechanism combines secure aggregation and differential privacy in a decentralized federated learning framework.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This study examines the theoretical, structural, and empirical applications of Artificial Intelligence (AI) and Machine Learning (ML) architectures within the domain of regulatory compliance (RegTech) and supervisory technology (SupTech) for cross-border digital transactions. The exponential expansion of cross-border financial flows, real-time payment systems, and decentralized financial instruments has amplified regulatory fragmentation, multi-jurisdictional compliance friction, and sophisticated financial crime typologies. Utilizing institutional economics, information asymmetry theory, and computational compliance modeling, this paper analyzes how advanced algorithmic architectures—specifically Graph Neural Networks (GNNs), Natural Language Processing (NLP), and Federated Learning—optimize anti-money laundering (AML), counter-terrorist financing (CFT), and real-time sanctions screening. The findings demonstrate that shifting from legacy rule-based heuristics to adaptive, privacy-preserving AI frameworks significantly compresses false-positive rates, bridges cross-jurisdictional regulatory disparities, and establishes a dynamic, mathematically rigorous paradigm for global financial integrity.
Vakhabov Bobur· Zenodo (CERN European Organi...· 0 citations
Achieving strong differential privacy guarantees within the constraints of federated learning, especially when utilizing complex models, remains a significant challenge. This work proposes a novel framework leveraging Lagrangian relaxation to address this issue. The core idea involves incorporating a Lagrangian term directly into the federated learning objective function to formally represent the differential privacy constraint. This allows for an iterative solution of the resulting Lagrangian problem via distributed optimization, providing a controllable mechanism for balancing privacy and model accuracy. The proposed approach offers a more practical and scalable solution compared to existing methods, particularly in scenarios where precise control over the privacy-accuracy trade-off is desired. The effectiveness of this method is demonstrated through theoretical analysis and conceptual discussion, outlining a pathway for future research and implementation.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Modern eCommerce and enterprise systems have become more complex, which has prompted the use of the microservice architecture and sophisticated data integration strategies to provide scalability and resilience, as well as speed in the delivery of features. This chapter consolidates important architectural patterns, integration frameworks and operational models, assesses their advantages and shortcomings, and also provides some future research areas such as AI-based orchestration of microservices, policy-as-code to automate governance, edge-native integration and consistent performance testing suites. These lessons can inform researchers and practitioners on how to develop well-tested and highly scalable networks that can support the changing needs of modern online companies. In the future, the application of transfer learning techniques to generalize models in other application contexts and federated learning schemes to privately share models between organizational units while masking data privacy should be investigated.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
MIT News · Artificial Intelligence· news.mit.eduAug 24, 2026
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
