Federated Learning (FL) distributes machine learning (ML) training across edge devices to reduce data transfer overhead and protect data privacy. Since FL model training may span hundreds of devices and is thus resource- and energy-intensive, it has a significant carbon footprint. Importantly, since energy's carbon-intensity differs substantially (by up to 60x) across locations, training on the same device using the same amount of energy, but at different locations, can incur widely different carbon emissions. While prior work has focused on improving FL's resource- and energy-efficiency by optimizing time-to-accuracy, it implicitly assumes all energy has the same carbon intensity and thus does not optimize carbon efficiency, i.e., work done per unit of carbon emitted. To address the problem, we design EcoLearn, which minimizes FL's carbon footprint without significantly affecting model accuracy or training time. EcoLearn achieves a favorable tradeoff by integrating carbon awareness into multiple aspects of FL training, including i) selecting clients with high data utility and low carbon, ii) provisioning more clients during the initial training rounds, and iii) mitigating stragglers by dynamically adjusting client over-provisioning based on carbon. We implement EcoLearn and its carbon-aware FL training policies in the Flower framework and show that it reduces the carbon footprint of training (by up to 10.8x) while maintaining model accuracy and training time (within similar to 1%) compared to state-of-the-art approaches.
While consciousness has been historically a heavily debated topic, awareness had less success in raising the interest of scholars. However, more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices connected with projects funded by the EIC Pathfinder Challenge “Awareness Inside” callwithin Horizon Europe, designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems.
This paper proposes a highly sustainable and scalable integrated AI-native architecture defining UNified archITecture for Open RAN-enabled Distributed, Scalable and SustainabilitY-enhanced 6G Networks (UNITY-6G) project that can support the diverse requirements of 6G networks by relying on advanced technologies, such as distributed ledger technology, semantic communications, digital network twinning to enhance the performance, cost-efficiency and trustworthiness of integrated 6G network services and applications. The focus is on scalability and sustainability for integrated networks (Non-Terrestrial and Terrestrial Networks, xHaul, Open RAN, Non-Public Networks, Edge, Core and Cloud). Furthermore, we aim to evolve to realtime distributed and network state-aware Open RAN that can leverage the integration of distributed applications in the integrated architecture. This will enable fine-grained data-driven management and control via incorporating dApps, distributed applications that complement existing xApps/rApps and use cases with stricter timing requirements in an integrated network. Common interfaces and protocols will be defined so that different heterogenous domains can communicate seamlessly. To better guide the design, This paper also use the principles of service based architecture for integrated networks and leverage digital twins for network evaluation and considers four use cases targeting: i) Sustainable networks for disaster handling, (ii) Immersive Experience with Real-time XR/holographic communications, (iii) Digital Twin for Integrated 6G Network Evaluation, (iv) MultiRAT O-RAN enabled NPN for supporting time sensitive applications for Industry 4.0.
Large Language Model (LLM) training generates synchronized Remote Direct Memory Access (RDMA) bursts that heavily stress datacenter fabrics and are highly sensitive to faults. However, access to full-scale training clusters is costly, and existing network testers fail to accurately reproduce such patterns. We introduce GPTraffic, a topology- and model-aware testing framework that predicts, emulates, and analyzes LLM training workloads on programmable hardware. By combining burst-accurate traffic generation, RDMA-aware semantics, and fine-grained fault injection, GPTraffic enables scalable, realistic, and reproducible experiments that faithfully reflect the dynamics of distributed LLM training. This allows researchers to explore performance bottlenecks, congestion behavior, and fault tolerance under conditions that closely mirror real-world AI training workloads.
W artykule jest przedstawiona telefoniczna działalność pomocowa Stowarzyszenia na Rzecz Przeciwdziałania Samotności wśród Ludzi Dorosłych „Telefon Pogadania” zakładająca wspieranie osób starszych, samotnych, w tym mieszkających na wsi, gdzie dostęp do interakcyjnego wsparcia może być ograniczony Zawiera on psychospołeczne uzasadnienie dla tej działalności, charakterystykę organizacyjną tej działalności, jej misję społeczną. Są w nim wskazane potencjały wspierania instytucji, organizacje i profesjonalistów sektora zdrowia w kultywowaniu relacji międzyludzkich i propagowaniu zdrowia psychicznego.
