Recent advances in distributed swarm learning (DSL) offer a promising paradigm for edge Internet of Things. Such advancements enhance data privacy, communication efficiency, energy saving, and model scalability. However, the presence of non-independent and identically distributed (non-i.i.d.) data pose a significant challenge for multi-access edge computing, degrading learning performance and diverging training behavior of vanilla DSL. Further, there still lacks theoretical guidance on how data heterogeneity affects model training accuracy, which requires thorough investigation. To fill the gap, this paper first study the data heterogeneity by measuring the impact of non-i.i.d. datasets under the DSL framework. This then motivates a new multi-worker selection design for DSL, termed M-DSL algorithm, which works effectively with distributed heterogeneous data. A new non-i.i.d. degree metric is introduced and defined in this work to formulate the statistical difference among local datasets, which builds a connection between the measure of data heterogeneity and the evaluation of DSL performance. In this way, our M-DSL guides effective selection of multiple works who make prominent contributions for global model updates. We also provide theoretical analysis on the convergence behavior of our M-DSL, followed by extensive experiments on different heterogeneous datasets and non-i.i.d. data settings. Numerical results verify performance improvement and network intelligence enhancement provided by our M-DSL beyond the benchmarks.

Swarm Learning (SL), a decentralized alternative to Federated Learning, offers privacy-preserving distributed training, but its reliance on blockchain technology hinders accessibility and scalability. This paper introduces a Simplified Peer-to-Peer Swarm Learning (P2P-SL) Frameworktailored for resource-constrained environments. By eliminating blockchain dependencies and adopting lightweight peer-to-peer communication, the proposed framework ensures robust model synchronization while maintaining data privacy. Applied to cancer histopathol-ogy, the framework integrates optimized pre-trained models, such as TorchXRayVision, enhanced with DenseNet decoders, to improve diagnostic accuracy. Extensive experiments demonstrate the framework's efficacy in handling imbalanced and biased datasets, achieving comparable performance to centralized models while preserving privacy. This study paves the way for democratizing advanced machine learning in healthcare, offering a scalable, accessible, and efficient solution for privacy-sensitive diagnostic applications. Keywords: Single-cell Sequencing Integration Multi-Omics Dimensionality Reduction Normalization. 1 Introduction The exponential growth in healthcare data, coupled with advancements in machine learning, has catalyzed significant progress in medical diagnostics [2,5,8]. However, challenges such as data privacy, imbalanced datasets, and the lack of interoperable frameworks continue to hinder the effective adoption of artificial arXiv:2504.16732v1

Deep learning models have raised privacy and security concerns due to their reliance on large datasets on central servers. As the number of Internet of Things (IoT) devices increases, artificial intelligence (AI) will be crucial for resource management, data processing, and knowledge acquisition. To address those issues, federated learning (FL) has introduced a novel approach to building a versatile, large-scale machine learning framework that operates in a decentralized and hardware-agnostic manner. However, FL faces network bandwidth limitations and data breaches. To reduce the central dependency in FL and increase scalability, swarm learning (SL) has been proposed in collaboration with Hewlett Packard Enterprise (HPE). SL represents a decentralized machine learning framework that leverages blockchain technology for secure, scalable, and private data management. A blockchain-based network enables the exchange and aggregation of model parameters among participants, thus mitigating the risk of a single point of failure and eliminating communication bottlenecks. To the best of our knowledge, this survey is the first to introduce the principles of Swarm Learning, its architectural design, and its fields of application. In addition, it highlights numerous research avenues that require further exploration by academic and industry communities to unlock the full potential and applications of SL.

In some ways it's a redux of the early days – in 1956, to be precise – immediately following the coining of the term "AI", at Dartmouth College in New Hampshire, USA. Back then, AI investments among competing countries created an effective AI "arms race". There were controversies, with early applications being developed in gaming, robotics, and autonomous vehicles. Well, innovation is either something new, or something nobody remembers. But today's modern AI is a mixture of both, and HPE and Intel have the something new part.

Swarm learning is one of the latest in a series of buzzwords that seem to continually appear. Along with artificial intelligence and machine learning, this latest phrase seems to be getting more and more "out there." Let's go deeper and find out… The name swarm learning was inspired by the collective behavior of animals and insects that come together to achieve a mutual goal. Think of bees swarming to build a hive, small fish forming a bait ball to scare off larger predator fish, wolves hunting their prey in packs, or birds moving together in flight. By joining together, insects and animals pool their resources and work together to collectively increase their ability to accomplish a goal.

HPE has released a pair of systems designed to broaden the uptake and speed deployment of machine learning among enterprises. Swarm Learning is aimed at bringing the wisdom of crowds to machine learning modeling without sacrificing security, while the Machine Learning Development System is meant to offer a one-box training solution for companies that would otherwise have had to design and build their own machine learning infrastructure. The Machine Learning Development System is available in physical footprints of several different sizes, but the company says a "small configuration" uses an Apollo 6500 Gen10 compute server to provide the horsepower for machine learning training, HPE ProLiant DL325 servers and Aruba CX 6300 switches for management of system components, and NVIDIA's Quantum InfiniBand networking platform, along with HPE's specialist Machine Learning Development Environment and Performance Cluster management software suites. According to IDC research vice president Peter Rutten, it's essentially bringing HPC (high performance computing) capabilities to enterprise machine learning, something that would usually require enterprises to architect their own systems. "It is the kind of system that businesses are really looking for, now that AI is more mature," he said.

Social media has become an effective platform to generate and spread fake news that can mislead people and even distort public opinion. Centralized methods for fake news detection, however, cannot effectively protect user privacy during the process of centralized data collection for training models. Moreover, it cannot fully involve user feedback in the loop of learning detection models for further enhancing fake news detection. To overcome these challenges, this paper proposed a novel decentralized method, Human-in-the-loop Based Swarm Learning (HBSL), to integrate user feedback into the loop of learning and inference for recognizing fake news without violating user privacy in a decentralized manner. It consists of distributed nodes that are able to independently learn and detect fake news on local data. Furthermore, detection models trained on these nodes can be enhanced through decentralized model merging. Experimental results demonstrate that the proposed method outperforms the state-of-the-art decentralized method in regard of detecting fake news on a benchmark dataset.

Artificial intelligence (AI) is turning out to be a major source of innovation, disruption, and competitive advantage in today's business environment. According to research and advisory firm Gartner Inc., AI will create $1.9 trillion of business value and 6.2 billion hours of worker productivity in 2021. One of the innovations brought about by AI is Swarm Learning (or SL) – a machine learning model that can help detect patients with severe illnesses such as leukaemia, tuberculosis, and COVID-19. It can also enable collaboration models in intelligent edge, autonomous vehicles, and cross-enterprise collaboration. To find out more about SL, the technology behind it, and how the model works, Frontier Enterprise recently talked with Dr. Eng Lim Goh, Senior Vice President and Chief Technology Officer for Artificial Intelligence at Hewlett Packard Enterprise (HPE).

So there's the first bias, which is, if you are learning in isolation, the hospital is learning, a neural network model, or a machine learning model, more generally, of a hospital is learning in isolation only on their own private patient data, they will be naturally biased towards the demographics they are seeing. For example, we have an example where a hospital trains their machine learning models on chest x-rays and sees a lot of tuberculosis cases. But very little of lung collapsed cases. So therefore, this neural network model, when trained, will be very sensitive to what's detecting tuberculosis and less sensitive towards detecting lung collapse, for example. However, we get the converse of it in another hospital. So what you really want is to have these two hospitals combine their data so that the resulting neural network model can predict both situations better. But since you can't share that data, swarm learning comes in to help reduce that bias of both the hospitals.

Science and medicine are becoming increasingly digital. Analyzing the resulting volumes of information -- known as "big data" -- is considered a key to better treatment options. "Medical research data are a treasure. They can play a decisive role in developing personalized therapies that are tailored to each individual more precisely than conventional treatments," said Joachim Schultze, Director of Systems Medicine at the DZNE and professor at the Life & Medical Sciences Institute (LIMES) at the University of Bonn. "It's critical for science to be able to use such data as comprehensively and from as many sources as possible."