Why Federated Learning on Public Blockchains Is a Privacy Paradox We Must Solve

Blockchain's core value—verifiable state transitions—is its fatal flaw for FL. Every model update or aggregation proof becomes a public data point, enabling inference attacks and model extraction.

• Inference Attack Risk: Adversaries can reconstruct training data from gradients.
• Sybil Vulnerability: Open participation allows poisoning of the global model.
• Regulatory Block: GDPR 'right to be forgotten' is impossible on an immutable ledger.

The only viable path is to cryptographically prove correct FL computation without revealing the inputs or outputs. This shifts trust from the blockchain's transparency to the soundness of the ZK circuit.

• Privacy-Preserving Proofs: zk-SNARKs (e.g., Halo2, Plonky2) verify local training adherence.
• Selective Disclosure: Participants can prove data quality or source without revealing the data.
• On-Chain Consensus, Off-Chain Compute: Leverage networks like Espresso Systems or Aztec for private execution layers.

FL on blockchains needs a sustainable cryptoeconomic model. Generating ZK proofs is computationally expensive (~$0.01-$0.10 per proof), creating a cost barrier that naive token emissions won't solve.

• Proof Subsidization: Protocols must bake proof costs into model usage fees or data licensing.
• Work Token Design: Networks like Akash or Render show models for verifiable compute markets.
• Data as Equity: Contributors could receive model royalties, not just one-time payments.

Leading architectures avoid putting the core FL loop on-chain. They use the blockchain as a coordination and incentive layer, while private computation happens off-chain in trusted execution environments (TEEs) or secure enclaves.

• Oasis Network: Uses Confidential ParaTimes with TEEs for private smart contract execution.
• FedML Chain: On-chain records for contributions and rewards; off-chain aggregation.
• Key Limitation: Relies on hardware trust assumptions (e.g., Intel SGX) which have been breached.

Federated Learning requires aggregating model updates from private datasets. On a public chain like Ethereum or Solana, every gradient update is visible, enabling model inversion and membership inference attacks that can reconstruct the original training data.

• Data Leakage: A single malicious node can deanonymize sensitive inputs.
• Regulatory Block: Makes compliance with GDPR and HIPAA impossible.
• Incentive Collapse: No entity will contribute valuable private data without guarantees.

Protocols like Phala Network and Secret Network use Trusted Execution Environments (TEEs) and Secure Multi-Party Computation (sMPC) to compute on encrypted data. The blockchain coordinates the process, but the raw data and model updates never leave a secure enclave.

• Privacy-Preserving Aggregation: Model updates are computed inside TEEs, only the final encrypted aggregate is posted on-chain.
• Verifiable Computation: The blockchain cryptographically verifies the integrity of the off-chain computation.
• Compatible Incentives: Contributors are paid for compute or data without exposing their private inputs.

Projects like Modulus Labs and EZKL are pioneering the use of zk-SNARKs and zk-STARKs to prove the correctness of federated learning rounds. Participants can prove they performed a valid training step on their private data, without revealing the data or the intermediate gradients.

• Selective Disclosure: Prove contribution quality and adherence to rules (e.g., fair training).
• On-Chain Settlement: The proof is the only thing settled on-chain, enabling trustless slashing for malicious actors.
• Scalable Verification: zk-proof verification is constant time, avoiding the compute overhead of TEE-based networks.

The public blockchain's core value is not computation, but coordination and incentive alignment. Protocols like Gensyn (which uses cryptographic proof systems) leverage chains like Ethereum to orchestrate a global network of compute nodes, staking, and payments for federated learning tasks.

• Global Marketplace: Matches data providers, model trainers, and verifiers.
• Cryptoeconomic Security: Staking and slashing ensure honest participation in the federated process.
• Sovereign Data Ownership: Contributors maintain control, selling compute on their encrypted data, not the data itself.

Aggregating model updates on-chain defeats the privacy purpose. Every gradient update, even encrypted, becomes a public data point for inference attacks. The blockchain's immutable ledger creates a permanent, analyzable dataset of model behavior.

• Verifiability vs. Opacity: The need to prove honest aggregation (e.g., via zk-SNARKs) adds massive computational overhead.
• Cost Prohibitive: Storing and computing on ~1-10MB model updates per round at $5-50+ per transaction makes continuous training economically impossible.

Public networks lack native identity. Without it, federated learning is vulnerable to Sybil attacks where a single entity controls multiple nodes to poison the model, and free-riders who consume the global model without contributing quality data.

• No Cost of Corruption: Pseudonymous wallets have no reputational stake. Attack cost is just gas fees.
• Incentive Misalignment: Projects like Ocean Protocol struggle with similar data marketplace issues. Pure token rewards attract low-quality, adversarial participation.

Blockchain consensus (e.g., ~12s Ethereum, ~2s Solana) is orders of magnitude slower than federated learning rounds in traditional settings (~100-500ms). Waiting for finality for each aggregation step cripples training speed, making real-time or even daily model updates impractical.

• Slow Feedback Loop: Model convergence could take months instead of days, rendering the model obsolete for fast-moving domains.
• Throughput Limits: Even high-TPS chains like Solana or Monad would be bottlenecked by state growth from update data, not just transactions.

GDPR 'Right to Be Forgotten' and data sovereignty laws are fundamentally incompatible with immutable blockchains. A user's data contribution, even as an encrypted gradient, cannot be truly deleted from the chain's history.

• Immutable Liability: The ledger becomes a permanent compliance risk.
• Jurisdictional Fog: Global, permissionless nodes process data subject to conflicting local laws (e.g., EU GDPR vs. US CLOUD Act). Projects like Secret Network face similar existential regulatory scrutiny.