{"product_id":"kit-pi-5-federated-learning-research-node","title":"Pi 5 Federated Learning Research Node","description":"\u003ch1\u003ePi 5 Federated Learning Research Node – Privacy-Preserving ML Cluster with Flower\u003c\/h1\u003e\n \n\u003cp class=\"value-summary\"\u003eEvery part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.\u003c\/p\u003e\n \n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Advanced\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 8-10 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 18-25\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Federated Learning with Flower Framework\u003c\/span\u003e\n\u003c\/div\u003e\n \n\u003cp\u003eDeploy a three-node federated learning testbed on your desk. Using the Flower framework, each Raspberry Pi 5 node trains a global model collaboratively on locally stored data—no raw samples ever leave the device. This is the same paradigm used in healthcare, finance, and edge AI research to protect sensitive information.\u003c\/p\u003e\n \n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eA fully networked federated learning cluster where three Pi 5 nodes run Flower client-server workloads. You'll configure NVMe SSDs for high-speed local data storage, connect them over a managed switch, and orchestrate model aggregation on a virtual server node. By the end, you'll run a real federated training loop—watching the shared model accuracy rise without centralizing datasets.\u003c\/p\u003e\n \n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eSetting up a Raspberry Pi 5 cluster with NVMe storage acceleration\u003c\/li\u003e\n  \u003cli\u003eInstalling and configuring the Flower federated learning framework\u003c\/li\u003e\n  \u003cli\u003eDesigning a privacy-preserving training pipeline with simulated clients\u003c\/li\u003e\n  \u003cli\u003eManaging distributed model aggregation and evaluating convergence\u003c\/li\u003e\n\u003c\/ul\u003e\n \n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRaspberry Pi 5 4GB\u003c\/td\u003e\n\u003ctd\u003e3\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eNVMe SSD 128GB\u003c\/td\u003e\n\u003ctd\u003e3\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePi 5 M.2 HAT+\u003c\/td\u003e\n\u003ctd\u003e3\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eEthernet Switch 5-port\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB-C PSU\u003c\/td\u003e\n\u003ctd\u003e3\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n \n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre-tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step-by-step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3-5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n \n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eIdeal for B.Tech CSE\/ECE majors taking up final-year projects on privacy-preserving AI, M.Tech researchers prototyping edge federated learning, and teams participating in the Smart India Hackathon’s privacy track. Also suited for ATL tinkering labs that want to demonstrate modern distributed ML concepts and IIT\/NIT\/VIT\/BITS students building publication-ready experiments.\u003c\/p\u003e\n \n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.\u003c\/p\u003e\n \n\u003cdetails\u003e\u003csummary\u003eWhat if I get stuck during the build?\u003c\/summary\u003e\u003cp\u003eScan the QR code on the box to chat with your AI build companion trained on this exact cluster setup, or reach our support team over WhatsApp for real-time assistance.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDo I need an internet connection to run federated learning?\u003c\/summary\u003e\u003cp\u003eThe cluster works on a local network for training; internet is only required for initial package installation and accessing the AI companion. Once set up, all training data stays offline and private.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I use this kit for real-world datasets like medical or financial data?\u003c\/summary\u003e\u003cp\u003eAbsolutely. The architecture is designed to keep raw data on each node. You can load real datasets onto the NVMe drives and run the Flower pipeline without any data leaving the device, making it compliant with privacy-by-design principles.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eWhat if a Raspberry Pi 5 fails during training?\u003c\/summary\u003e\u003cp\u003eFederated learning is resilient to client dropouts. The Flower framework will continue aggregation with the remaining nodes. If a hardware fault occurs, contact us for a replacement under the 7-day defect policy.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eFlower federated learning framework trains a shared model across three Pi 5 nodes without sharing raw data — privacy-preserving ML.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/raspberry-pi-5-model-b-4gb-technical-specs-projects\"\u003eRaspberry Pi 5 4GB\u003c\/a\u003e x3\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/official-raspberry-pi-m2-hat-nvme-ssd-add-on-board-for-pi-5\"\u003eNVMe SSD 128GB\u003c\/a\u003e x3\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/raspberry-pi-5-pcie-to-m2-nvme-ssd-expansion-board-by-elecrow\"\u003ePi 5 M.2 HAT+\u003c\/a\u003e x3\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/wt32-eth01-evo-esp32-ethernet-development-board\"\u003eEthernet Switch 5-port\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\n\u003ca href=\"\/products\/raspberry-pi-4-official-power-supply-5v-3a-usb-c-compoden\"\u003eUSB-C PSU\u003c\/a\u003e x3\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Pi 5 Federated Learning Research Node?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 Federated Learning Research Node includes all components needed: Raspberry Pi 5 4GB, NVMe SSD 128GB, Pi 5 M.2 HAT+, Ethernet Switch 5-port, USB-C PSU and more. Everything is pre-tested for compatibility and shipped from Bengaluru, India.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What skill level is required for the Pi 5 Federated Learning Research Node?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Advanced level makers, suitable for ages 18-25. Flower federated learning framework trains a shared model across three Pi 5 nodes without sharing raw data — privacy-preserving ML. Estimated build time is 8-10 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 Federated Learning Research Node online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 Federated Learning Research Node is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. Ships from Bengaluru in 1-5 business days across India.\"\n      }\n    }\n  ]\n}\n\u003c\/script\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"Product\",\n  \"name\": \"Pi 5 Federated Learning Research Node\",\n  \"description\": \"Flower federated learning framework trains a shared model across three Pi 5 nodes without sharing raw data — privacy-preserving ML.\",\n  \"sku\": \"CDN-KIT-2559\",\n  \"brand\": {\"@type\": \"Brand\", \"name\": \"Compoden\"},\n  \"offers\": {\n    \"@type\": \"Offer\",\n    \"url\": \"https:\/\/compoden.in\/products\/kit-pi-5-federated-learning-research-node\",\n    \"priceCurrency\": \"INR\",\n    \"price\": \"75470\",\n    \"availability\": \"https:\/\/schema.org\/InStock\",\n    \"seller\": {\"@type\": \"Organization\", \"name\": \"Compoden\"}\n  },\n  \"category\": \"Edge AI \u0026 Computer Vision\"\n}\n\u003c\/script\u003e","brand":"Compoden","offers":[{"title":"Default Title","offer_id":53469369401709,"sku":"CDN-KIT-2559","price":89050.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0999\/3997\/5533\/files\/kit-pi-5-federated-learning-research-node.png?v=1781948407","url":"https:\/\/compoden.com\/products\/kit-pi-5-federated-learning-research-node","provider":"Compoden","version":"1.0","type":"link"}