Training large language models (LLMs) at the network edge faces fundamental challenges arising from device resource constraints, severe data heterogeneity, and heightened privacy risks. To address these, we propose ELSA (Efficient LLM-centric Split Aggregation), a novel framework that systematically integrates split learning (SL) and hierarchical federated learning (HFL) for distributed LLM fine-tuning over resource-constrained edge networks. ELSA introduces three key innovations. First, it employs a task-agnostic, behavior-aware client clustering mechanism that constructs semantic fingerprints using public probe inputs […]

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In the 1980s, the rock band Van Halen famously included a bizarre clause in their tour rider: a bowl of M&Ms in the dressing room with absolutely no brown ones. For years, the media mocked this as the ultimate example of rock star excess. But lead singer David Lee Roth later revealed the brilliant logic behind it. It wasn’t about the candy. It was a compliance test. Van Halen’s production involved massive stages, high-voltage electronics, and heavy rigging. If the […]

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Third party services SDKs and their circumstances Developing and publishing games often involves relying on third-party services, middleware, or platforms. Numerous vendors offer commercial products like advertising networks, backend services, sound engines, user authentication, and more. These tools simplify the process of monetization, expanding player reach, storing data on servers, and performing other functions beyond what a game engine typically provides. To facilitate integration, these vendors often supply SDKs tailored for specific game engines, allowing developers to easily […]

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Introduction Today’s global digital platforms are powered by hundreds of microservices that run behind the frontend that users are exposed to. These services all have to operate at scale in conjunction with each other. Hence, the ultimate user experience is determined by the composite availability of these systems, engineered so that the final service continues to operate even if subsystems experience outages. Talking about availability standards of 5 9s, systems that are available 99.999% of the time are […]

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This paper describes a new auto-associative network called a Unit-Merge Network. It is so-called because novel compound keys are used to link 2 nodes in 1 layer, with 1 node in the next layer. Unit nodes at the base store integer values that can represent binary words. The word size is critical and specific to the dataset and it also provides a first level of consistency over the input patterns. A second cohesion network then links the unit […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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Stop the memory rot TL;DR: You can keep your AI sharp by forcing it to summarize and prune what it remembers (a.k.a. compacting). Common Mistake ❌ You keep a single, long conversation open for hours. You feed the AI with every error log and every iteration of your code. Eventually, the AI starts to ignore your early instructions or hallucinate nonexistent functions. Problems Addressed 😔 Context Decay: The AI loses track of your original goals in the middle of […]

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Hi, I have fairly successful repository https://github.com/maitbayev/the-elements-of-statistical-learning that contains my notes for the book via a series of Jupyter notebooks. To make the notes easier to navigate and study, I have deployed a much cleaner and more structured format here: https://maitbayev.github.io/esl/ Thanks submitted by /u/madiyar [link] [comments]

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arXiv:2503.03283v2 Announce Type: replace Abstract: Drawing parallels with the way biological networks are studied, we adapt the treatment–control paradigm to explainable artificial intelligence research and enrich it through multi-parametric input alterations. In this study, we propose a framework for investigating the internal inference impacted by input data augmentations. The internal changes in network operation are reflected in activation changes measured by variance, which can be decomposed into components related to each augmentation, employing Sobol indices and Shapley values. […]

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