Dataset of round-level federated learning and layer-2 blockchain overhead from ten raspberry Pi edge clients

This data article describes round-level records from 40 federated learning sessions run on a physical testbed of ten Raspberry Pi 4 Model B devices acting as edge clients, with an Ubuntu workstation as the aggregation server. Each session trains a one-dimensional Squeeze-and-Excitation ResNet classifier over ten aggregation rounds using the Flower framework and commits every round to a smart contract on Base mainnet, a public Ethereum-compatible Layer-2 chain, while pinning the model and metric artifacts to IPFS through Pinata. The sessions cover two human activity recognition benchmarks, MHEALTH and UCI-HAR, and two middleware configurations: a baseline that performs blockchain and IPFS operations synchronously with per-round gas estimation, and an optimised variant that overlaps IPFS uploads with local training and reuses cached gas parameters, giving 20 sessions per benchmark. For every round the dataset records server-side global metrics (accuracy, F1, AUC, and the confusion matrix), per-device training telemetry (loss, training time, CPU, memory, and temperature), per-client evaluation records, and the IPFS content identifiers and transaction hashes that link each round to Base mainnet. For every session, millisecond-resolution logs record the latency of each blockchain and storage operation. The repository contains 12 CSV files, two archives of raw session directories, and a data dictionary. Researchers can reuse the convergence traces to benchmark edge federated learning, the device telemetry to characterise resource use on constrained hardware, and the latency distributions to parameterise simulations of blockchain-enabled federated learning without physical deployments or transaction fees. All 1240 blockchain transactions remain publicly verifiable on the Base mainnet explorer.