Why Decentralized Storage Is a Prerequisite for Scalable Federated Learning on Blockchain

Federated learning requires a shared, immutable dataset for model verification and aggregation. Centralized storage is a single point of failure and manipulation, undermining the entire system's integrity.

• Data Provenance: No cryptographic proof of data origin or history.
• Censorship Risk: A central operator can withhold or alter training data.
• Vendor Lock-in: Creates dependency on a single entity's infrastructure.

Arweave's permaweb provides a one-time payment for permanent, on-chain data storage. This is critical for storing canonical model checkpoints and training datasets that must be immutable for verifiability.

• Data Permanence: Pay once, store forever. Eliminates recurring cost risk for foundational datasets.
• Content-Addressable: Data is referenced by its hash, guaranteeing integrity.
• Incentive Alignment: Miners are rewarded for replicating and storing the entire dataset.

While IPFS provides content-addressed storage and p2p delivery, Filecoin adds a cryptoeconomic layer for verifiable, long-term storage deals. This combo is ideal for distributing large model updates in federated rounds.

• Proven Storage: Storage providers cryptographically prove they hold the data.
• Dynamic Pricing: A competitive marketplace for storage and retrieval costs.
• High Throughput: Optimized for delivering large files (e.g., model checkpoints) across a global network.

For federated learning executed on L2s or app-chains, Data Availability (DA) is non-negotiable. These layers ensure training data and model state transitions are published and available for verification, preventing fraud.

• Scalable DA: Decouples execution from data publishing, reducing costs for high-volume ML data.
• Restaking Security: EigenLayer allows re-staked ETH to secure DA layers, inheriting Ethereum's trust.
• Modular Design: Enables specialized FL chains to outsource costly data storage.

Storing model checkpoints and gradients directly on L1/L2 is economically impossible. A single 1GB model update on Ethereum would cost ~$200k+ at 20 gwei. This kills any scalable training loop.

• Cost Prohibitive: Gas fees make iterative model updates financially absurd.
• Throughput Bottleneck: Block space cannot handle the data volume of continuous learning.
• State Bloat: Full nodes choke on non-essential, bulky training data.

Protocols like Filecoin, Arweave, and Celestia's Blobstream act as a verifiable data availability (DA) layer. Store massive datasets off-chain, post cryptographic commitments (e.g., Merkle roots) on-chain.

• Cost Shift: Pay ~$0.01/GB for persistent storage vs. L1 gas.
• Verifiable Integrity: On-chain proofs guarantee data hasn't been tampered with for training.
• Composability: Smart contracts (e.g., on Ethereum, Solana) can permission and trigger training jobs based on proven data availability.

Decentralized storage enables proof-carrying data workflows. A zkML coprocessor (like Risc Zero, EZKL) can generate a ZK proof that a model was trained correctly on the committed data, without revealing the raw data.

• Privacy-Preserving: Raw user data never leaves the storage node; only aggregated updates or proofs do.
• Trust Minimization: Validators verify the ZK proof, not the entire computation, enabling ~1-2 order magnitude cheaper on-chain settlement.
• Interoperability: This pattern works across EigenLayer AVSs, Babylon's Bitcoin staking, or any smart contract chain needing verified ML.

Using AWS S3 reintroduces a trusted intermediary, breaking the decentralized trust model. The blockchain becomes a pointless oracle to a centralized black box.

• Censorship Risk: A single entity can alter, withhold, or censor training data.
• Verification Overhead: You must trust AWS's audit logs, not cryptographic guarantees.
• Economic Leakage: Value accrues to cloud providers, not the protocol's tokenomic stakeholders. Projects like Filecoin and Arweave align storage incentives with the network's security.