OceanWeave framework tackles blockchain trilemma with federated learning and sharded compute-to-data consensus

A new framework called OceanWeave claims to ease the long-standing blockchain trilemma—the difficulty of balancing scalability, security, and decentralization—by combining Ocean Protocol’s decentralized data markets with sharded Compute-to-Data (C2D) consensus. The work, published in Cluster Computing, introduces a design that pairs storage-backed Proof-of-Data-Compliance (PoDC) with federated learning encrypted under the BGV (Brakerski-Gentry-Vaikuntanathan) homomorphic scheme, a privacy-preserving machine learning paradigm in which models are trained across decentralized nodes without raw data ever being exchanged.

According to the paper’s reported experimental results, OceanWeave achieves 3,200 transactions per second (TPS) at 380 milliseconds of latency across a network of 2,048 nodes. The authors state this is 4.8 times faster than Filecoin and 188 times Ethereum’s raw transaction throughput for simple transfers. They are careful to note that the comparison narrows when the workload changes: for verifiable compute jobs, OceanWeave’s advantage remains significant but drops to approximately 12 times after normalizing for operation complexity, a distinction that matters because simple-transfer benchmarks flatter any system relative to general-purpose computation.

Robustness against faulty or malicious participants is a central claim. The system maintains 98 percent model accuracy even when up to 35 percent of nodes are Byzantine—that is, malicious or faulty actors—a property the authors attribute to the combination of federated learning over encrypted updates and the sharded consensus layer. In federated learning, no participant needs to surrender its raw data; only model updates travel, and homomorphic encryption allows computation on those updates while they remain encrypted, which the authors position as enabling GDPR-compliant AI and machine learning pipelines for decentralized finance (DeFi) and Internet of Things ecosystems.

Storage economics form the other pillar of the framework. Through erasure-coded sharding—a data redundancy technique that splits data into k=4 fragments with m=2 parity blocks, each replicated across three nodes for geographic durability—the architecture reportedly reduces storage costs to $0.004 per gigabyte, which the authors describe as 74 percent cheaper than AWS S3 and 50 percent cheaper than Filecoin. Decentralized pricing oracles are used to set marketplace prices rather than a central operator. On cost efficiency per unit of throughput, the paper reports $0.003 per TPS compared with AWS’s $0.023.

The scalability results extend further in the authors’ experiments: linear scaling to 4,800 TPS at 2,048 nodes, with hardware costs of $220 described as democratizing participation, and throughput stated as 3.2 times that of Solana. The authors position OceanWeave as the first platform to effectively mitigate the trilemma’s trade-offs while supporting compliant decentralized machine learning. As with any system evaluated by its own authors in a single publication, the reported throughput, cost, and Byzantine-tolerance figures represent the paper’s experimental claims within its tested configuration of 2,048 nodes; independent replication at larger scales and under adversarial conditions beyond the reported 35 percent Byzantine threshold would be needed to confirm how the framework performs in production decentralized networks.

Subject of Research: A decentralized data marketplace framework using federated learning and sharded compute-to-data consensus to address the blockchain trilemma

Article Title: Resolving the blockchain trilemma via federated learning and ocean protocol: a decentralized data marketplace framework with sharded compute-to-data consensus

Article References: Reno, S., Roy, K., & Shafin, K. M. (2026). Resolving the blockchain trilemma via federated learning and ocean protocol: a decentralized data marketplace framework with sharded compute-to-data consensus. Cluster Computing, 29(15), Article 876. https://doi.org/10.1007/s10586-026-06725-2

Image Credits: AI Generated

DOI: 10.1007/s10586-026-06725-2

Keywords: blockchain trilemma, OceanWeave, Ocean Protocol, federated learning, homomorphic encryption, compute-to-data, sharding, erasure coding, Byzantine fault tolerance, decentralized data marketplace, GDPR compliance, throughput