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DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Self-Sovereign Identity and eIDAS 2.0: An Analysis of Con...
Nacereddine Sitouah, Marco Esposito, Francesco Bruschi · 2026-01-28 · via cs.DC updates on arXiv.org

European digital identity initiatives are grounded in regulatory frameworks designed to ensure interoperability and robust, harmonized security standards. The evolution of these frameworks culminates in eIDAS 2.0, whose origins trace back to the Electronic Signatures Directive 1999/93/EC, the first EU-wide legal foundation for the use of electronic signatures in cross-border electronic transactions. As technological capabilities advanced, the initial eIDAS 1.0 framework was increasingly criticized for its limitations and lack of comprehensiveness. Emerging decentralized approaches further exposed these shortcomings and introduced the possibility of integrating innovative identity paradigms, such as Self-Sovereign Identity (SSI) models. In this article, we contribute to the ongoing legal and policy debate on the European Digital Identity Framework by analyzing key provisions of eIDAS 2.0 and its accompanying recitals, drawing on a systematic literature review guided by defined Research Questions (RQ). This work employs a structured methodological approach that combines descriptive and comparative analysis, systematic gap analysis supported by a defined scoring matrix, and normative analysis to evaluate the compatibility of SSI properties with eIDAS 2.0 regulation, as operationalized via its Architecture and Reference Framework (ARF). Furthermore, we assess the ARF's guidelines and examine the extent to which it aligns with SSI. The analysis adopts a complementary perspective demonstrating how the regulation can be further developed to better support SSI in the future by identifying existing limitations and potential adoption opportunities within the current legal foundations of the framework.