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Quantum Threats to Federated Learning and Privacy-Preserving AI

Assessing quantum attack surfaces on federated learning architectures and homomorphic encryption schemes.

Full day (6 hours)

Certificate on completion

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Course Overview

For AI researchers, privacy engineers, and FL platform architects. Covers secure aggregation protocol vulnerabilities under quantum attack, HE scheme quantum resistance assessment, differential privacy channel exposure, PQC migration for TFF, PySyft, and FATE, and framework-specific migration paths.

Curriculum

Course content is structured into sessions below. Each session is configurable to your organisation's environment, threat model, and objectives.

#	Session	Topics
1	Federated Learning Architecture and Its Cryptographic Surface Where cryptography lives in FL pipelines
2	Secure Aggregation Protocols Under Quantum Attack How Shor and Grover threaten FL trust assumptions	Secure aggregation in TensorFlow Federated, PySyft, and FATE: protocol-level cryptographic dependencies (Diffie-Hellman key agreement, secret sharing, threshold encryption) Shor's algorithm impact: ECDH-based key exchange in aggregation collapses under a cryptographically relevant quantum computer, exposing individual model updates Grover's algorithm impact: reduced effective key strength for symmetric ciphers protecting aggregated gradients in transit and at rest
Break
3	Homomorphic Encryption and Differential Privacy in a Quantum Context Assessing quantum resistance of privacy-preserving primitives	Lattice-based HE schemes (CKKS, BFV, BGV): inherent quantum resistance from lattice hardness assumptions versus known quantum attacks on ideal lattice structures Differential privacy mechanisms: noise calibration assumptions hold under quantum adversaries, but the cryptographic channels delivering DP guarantees may not Secure multi-party computation (SMPC): identifying which sub-protocols rely on quantum-vulnerable key exchange and which use quantum-resistant primitives
4	Interactive Demonstration: FL Cryptographic Dependency Audit Full-day format only	Mapping cryptographic dependencies in a representative FL pipeline (TensorFlow Federated or PySyft) Classifying each dependency as quantum-vulnerable (ECDH, RSA), quantum-resistant (lattice-based HE), or uncertain (hybrid constructions) Producing a prioritised migration inventory with risk scores and migration sequencing
Break
5	PQC Migration for Federated Learning Infrastructure Replacing vulnerable primitives without breaking FL workflows	Replacing ECDH key agreement in secure aggregation with ML-KEM (FIPS 203): performance implications for aggregation latency across 100+ FL participants Migrating digital signatures on model updates and attestation from ECDSA to ML-DSA (FIPS 204) or SLH-DSA (FIPS 205) Hybrid migration strategies: running classical and post-quantum key exchange in parallel during transition without doubling communication overhead
6	Framework-Specific Migration Paths and Compliance Practical guidance for TFF, PySyft, and FATE deployments	TensorFlow Federated: gRPC channel encryption, aggregation server TLS configuration, and PQC library integration points PySyft: Duet protocol cryptographic dependencies, migration path for private tensor operations FATE: federated transfer learning encryption layer, CKKS parameter selection for post-quantum security margins
7	Q&A and Migration Planning

Designed and Delivered By

Training courses are designed and delivered by QSECDEF in collaboration with subject specialists. All instructors have direct experience in quantum technologies and security.

QSECDEF brings expertise in post-quantum cryptography, quantum computing strategy, and defence-grade security assessment. Our membership spans 900+ organisations and 1,500+ professionals working at the intersection of quantum technologies and critical infrastructure security.

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Quantum Threats to Federated Learning and Privacy-Preserving AI

Course Overview

Curriculum

Designed and Delivered By

Quantum Security Defence

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