Machine Unlearning: A Complete Technical Guide

Summary

We present a step-by-step system for machine unlearning, covering: (1) data sharding and provenance tracking; (2) three core unlearning algorithms (SISA, influence-based corrections, certified removal); (3) a RESTful unlearning API; (4) certification and monitoring procedures. Each component is linked to its original reference so you can dive straight into the implementation details.

1. Data Ingestion & Sharding

1. Data Provenance Registry
   • Record for every training sample: unique ID, shard assignment, slice order (arXiv).
2. Sharded Training Pipeline
   • Split dataset into N disjoint shards, each further sliced into M sequential versions.
   • Train one child model per slice, aggregating via ensembling or distillation (arXiv).

Link: Machine Unlearning (SISA) – arXiv:1912.03817

SISA Architecture

2. Unlearning Algorithms

2.1 Exact Shard-Isolation (SISA)

• Mechanism: Only the shard containing the "forget" sample is retrained; other shards remain unchanged (arXiv).
• Cost: O(1/N) of full retrain, with slice granularity controlling replay depth.
• Code Reference: see Appendix A for PyTorch SISA loop.

2.2 Influence-Function Corrections

• Principle: Use influence functions to estimate the parameter delta from deleting one sample via Hessian-vector products (Proceedings of Machine Learning Research).
• Trade-Off: Fast for convex or shallow models; approximation degrades on deep nets.
• Paper: Understanding Black-box Predictions via Influence Functions (ICML 2017)

2.3 Certified Removal

• Guarantee: Formal bound that the unlearned model is indistinguishable from one retrained from scratch without the forgotten data (Proceedings of Machine Learning Research).
• Approach: For linear models, apply a "removal mechanism" that perturbs parameters and certifies statistical closeness.
• Advanced: Newer methods extend to non-convex models via privacy amplification by post-processing (arXiv).

Link: Certified Data Removal (ICML 2020)

3. Unlearning API & Integration

1. REST Endpoint /unlearn

   POST /unlearn
   Content-Type: application/json
   
   {
     "sample_id": "UUID-1234",
     "method": "sisa" | "influence" | "certified"
   }

   Copy Copy
2. Dispatcher
   • Look up sample_id → shard s and slice k.
   • Trigger chosen algorithm on model(s):
     • SISA: retrain slice k of shard s.
     • Influence: compute gradient and Hessian corrections on global model.
     • Certified: apply certified removal mechanism.
3. Async Queue
   • Use a job queue (e.g. RabbitMQ) to manage heavy retraining tasks.
   • Provide immediate 202 Accepted and polling URL for status.

4. Certification & Monitoring

1. Gold Standard Comparison
   • Periodically retrain full model offline; compare key metrics (accuracy, loss) against unlearned model within ε-tolerances (IJCAI).
2. Immutable Audit Logs
   • Append every unlearning request and parameter-update hash to a tamper-evident ledger (USENIX).
3. Performance Dashboards
   • Track per-shard retrain latency and system throughput.
   • Alert if average unlearning time > SLA threshold.

Link: ARCANE: Exact Unlearning Architecture (IJCAI 2022)

5. End-to-End Example (PyTorch Snippet)

# Appendix A: SISA Retrain Loop
from torch.utils.data import DataLoader, Subset
def sisa_unlearn(model_class, full_dataset, shard_idx, slice_idx, epochs):
    # 1. Identify shard and slice
    shard_size = len(full_dataset) // N
    slice_size = shard_size // M
    start = shard_idx * shard_size + slice_idx * slice_size
    end   = start + slice_size

    # 2. Prepare data
    retrain_data = Subset(full_dataset, list(range(start, end)))
    loader = DataLoader(retrain_data, batch_size=64, shuffle=True)

    # 3. Retrain child model
    model = model_class()  
    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
    for _ in range(epochs):
        for x, y in loader:
            loss = F.cross_entropy(model(x), y)
            optimizer.zero_grad(); loss.backward(); optimizer.step()
    return model

Key Challenges in Machine Unlearning

While the methods above provide a foundation for machine unlearning, several practical challenges remain:

Computational Efficiency

Even with optimized approaches like SISA, unlearning at scale remains computationally expensive. For large-scale models and datasets, carefully balancing shard size with retraining cost is essential.

Verification Metrics

Determining when a model has truly "forgotten" data is complex. Empirical validation requires developing rigorous verification protocols that measure information leakage without compromising system performance.

Adversarial Considerations

The presence of malicious actors attempting to extract supposedly forgotten information presents additional security challenges, requiring robust defense mechanisms beyond basic unlearning procedures.

References

1. Bourtoule et al., "Machine Unlearning (SISA)," arXiv:1912.03817 (arXiv)
2. Koh & Liang, "Influence Functions," ICML 2017 (Proceedings of Machine Learning Research)
3. Guo et al., "Certified Data Removal," ICML 2020 (Proceedings of Machine Learning Research)
4. Thudi et al., "Auditable Definitions for Unlearning," USENIX Sec '22 (USENIX)
5. ARCANE: "An Efficient Architecture for Exact Unlearning," IJCAI 2022 (IJCAI)
6. Cao & Yang, "Summation-Form Unlearning," arXiv 2024 (arXiv)
7. Li et al., "Zero-Shot Unlearning via Noise Perturbations," OpenReview 2025 (OpenReview)
8. Arora et al., "Unlearning Challenge Insights," NeurIPS 2023 (unlearning-challenge.github.io)
9. Borji, "Real-World Machine Unlearning," Medium 2023 (Medium)
10. Zhang et al., "Certified Unlearning without Data," arXiv 2025 (arXiv)