Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 56 tok/s
Gemini 2.5 Pro 39 tok/s Pro
GPT-5 Medium 15 tok/s Pro
GPT-5 High 16 tok/s Pro
GPT-4o 99 tok/s Pro
Kimi K2 155 tok/s Pro
GPT OSS 120B 476 tok/s Pro
Claude Sonnet 4 38 tok/s Pro
2000 character limit reached

Exploring the Robustness of Decentralized Training for Large Language Models (2312.00843v1)

Published 1 Dec 2023 in cs.LG, cs.AI, and cs.CR

Abstract: Decentralized training of LLMs has emerged as an effective way to democratize this technology. However, the potential threats associated with this approach have not been carefully discussed, which would hinder the development of decentralized training infrastructures. This paper aims to initiate discussion towards this end by exploring the robustness of decentralized training from three main perspectives. First, we demonstrate the vulnerabilities inherent in decentralized training frameworks in terms of hardware, data, and models. Second, we highlight the fundamental difference between decentralized foundation model training and vanilla federated learning, where the security techniques employed in federated learning cannot be applied directly. Third, we discuss the essential components required for a robust and efficient decentralized training framework and present a case study by modeling a concrete threat model. Our objective in this vision paper is to emphasize the importance of addressing security concerns in the context of decentralized training for LLMs.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (75)
  1. Fixing by mixing: A recipe for optimal byzantine ml under heterogeneity. In International Conference on Artificial Intelligence and Statistics. PMLR, 1232–1300.
  2. Privacy-preserving deep learning via additively homomorphic encryption. IEEE transactions on information forensics and security 13, 5 (2017), 1333–1345.
  3. Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers. International Journal of Security and Networks 10, 3 (2015), 137–150.
  4. Varuna: scalable, low-cost training of massive deep learning models. In Proceedings of the Seventeenth European Conference on Computer Systems. 472–487.
  5. Fairscale: A general purpose modular pytorch library for high performance and large scale training.
  6. Vladimir A Bogatyrev and AV Bogatyrev. 2015. Functional reliability of a real-time redundant computational process in cluster architecture systems. Automatic Control and Computer Sciences 49 (2015), 46–56.
  7. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021).
  8. Towards federated learning at scale: System design. Proceedings of machine learning and systems 1 (2019), 374–388.
  9. Practical secure aggregation for privacy-preserving machine learning. In proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 1175–1191.
  10. Training transformers together. In NeurIPS 2021 Competitions and Demonstrations Track. PMLR, 335–342.
  11. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877–1901.
  12. Understanding distributed poisoning attack in federated learning. In 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 233–239.
  13. Privacy preserving distributed machine learning with federated learning. Computer Communications 171 (2021), 112–125.
  14. Chatlaw: Open-source legal large language model with integrated external knowledge bases. arXiv preprint arXiv:2306.16092 (2023).
  15. Byzantine spectral ranking. Advances in Neural Information Processing Systems 35 (2022), 27745–27756.
  16. Distributed deep learning in open collaborations. Advances in Neural Information Processing Systems 34 (2021), 7879–7897.
  17. BRIDGE: Byzantine-resilient decentralized gradient descent. IEEE Transactions on Signal and Information Processing over Networks 8 (2022), 610–626.
  18. Byzantine machine learning made easy by resilient averaging of momentums. In International Conference on Machine Learning. PMLR, 6246–6283.
  19. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557 (2017).
  20. Towards efficient and privacy-preserving federated deep learning. In ICC 2019-2019 IEEE international conference on communications (ICC). IEEE, 1–6.
  21. Soufiane Hayou and Fadhel Ayed. 2021. Regularization in resnet with stochastic depth. Advances in Neural Information Processing Systems 34 (2021), 15464–15474.
  22. Deep models under the GAN: information leakage from collaborative deep learning. In Proceedings of the 2017 ACM SIGSAC conference on computer and communications security. 603–618.
  23. Handcrafted backdoors in deep neural networks. Advances in Neural Information Processing Systems 35 (2022), 8068–8080.
  24. Personalized federated learning with differential privacy. IEEE Internet of Things Journal 7, 10 (2020), 9530–9539.
  25. Deep networks with stochastic depth. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14. Springer, 646–661.
  26. Lawyer LLaMA Technical Report. arXiv preprint arXiv:2305.15062 (2023).
  27. Gpipe: Efficient training of giant neural networks using pipeline parallelism. Advances in neural information processing systems 32 (2019).
  28. Technology Innovation Institute. 2023. Falcon 180B. https://falconllm.tii.ae/falcon-180b.html
  29. Oobleck: Resilient Distributed Training of Large Models Using Pipeline Templates. In Proceedings of the 29th Symposium on Operating Systems Principles (Koblenz, Germany) (SOSP ’23). Association for Computing Machinery, New York, NY, USA, 382–395. https://doi.org/10.1145/3600006.3613152
  30. Challenges and applications of large language models. arXiv preprint arXiv:2307.10169 (2023).
  31. On the privacy preserving properties of random data perturbation techniques. In Third IEEE international conference on data mining. IEEE, 99–106.
  32. Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing. In International Conference on Learning Representations.
  33. Learning from history for byzantine robust optimization. In International Conference on Machine Learning. PMLR, 5311–5319.
  34. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016).
  35. Backdoor Attacks on Pre-trained Models by Layerwise Weight Poisoning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 3023–3032. https://doi.org/10.18653/v1/2021.emnlp-main.241
  36. Shigang Li and Torsten Hoefler. 2021. Chimera: efficiently training large-scale neural networks with bidirectional pipelines. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 1–14.
  37. PyTorch distributed: experiences on accelerating data parallel training. Proceedings of the VLDB Endowment 13, 12 (2020), 3005–3018.
  38. Adaptive privacy-preserving federated learning. Peer-to-peer networking and applications 13 (2020), 2356–2366.
  39. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics. PMLR, 1273–1282.
  40. Learning Differentially Private Recurrent Language Models. In International Conference on Learning Representations. https://openreview.net/forum?id=BJ0hF1Z0b
  41. Byzantine preferential voting. In International Conference on Web and Internet Economics. Springer, 327–340.
  42. Payman Mohassel and Yupeng Zhang. 2017. Secureml: A system for scalable privacy-preserving machine learning. In 2017 IEEE symposium on security and privacy (SP). IEEE, 19–38.
  43. PipeDream: Generalized pipeline parallelism for DNN training. In Proceedings of the 27th ACM Symposium on Operating Systems Principles. 1–15.
  44. Efficient large-scale language model training on gpu clusters using megatron-lm. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 1–15.
  45. Emin Orhan and Xaq Pitkow. 2018. Skip Connections Eliminate Singularities. In International Conference on Learning Representations. https://openreview.net/forum?id=HkwBEMWCZ
  46. A case for redundant arrays of inexpensive disks (RAID). In Proceedings of the 1988 ACM SIGMOD international conference on Management of data. 109–116.
  47. Language models are unsupervised multitask learners. OpenAI blog 1, 8 (2019), 9.
  48. Zero: Memory optimizations toward training trillion parameter models. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 1–16.
  49. Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3505–3506.
  50. Aide: Fast and communication efficient distributed optimization. arXiv preprint arXiv:1608.06879 (2016).
  51. Swarm parallelism: Training large models can be surprisingly communication-efficient. arXiv preprint arXiv:2301.11913 (2023).
  52. SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient. In Proceedings of the 40th International Conference on Machine Learning (Proceedings of Machine Learning Research), Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (Eds.), Vol. 202. PMLR, 29416–29440. https://proceedings.mlr.press/v202/ryabinin23a.html
  53. Adi Shamir. 1979. How to share a secret. Commun. ACM 22, 11 (1979), 612–613.
  54. Hugginggpt: Solving ai tasks with chatgpt and its friends in huggingface. arXiv preprint arXiv:2303.17580 (2023).
  55. Megatron-lm: Training multi-billion parameter language models using model parallelism. arXiv preprint arXiv:1909.08053 (2019).
  56. Large language models encode clinical knowledge. arXiv preprint arXiv:2212.13138 (2022).
  57. Large language models encode clinical knowledge. Nature 620, 7972 (2023), 172–180.
  58. FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs. arXiv preprint arXiv:2309.01172 (2023).
  59. Byzantine-resilient federated learning at edge. IEEE Trans. Comput. (2023).
  60. Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large {{\{{DNNs}}\}}. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23). 497–513.
  61. Data poisoning attacks against federated learning systems. In Computer Security–ESORICS 2020: 25th European Symposium on Research in Computer Security, ESORICS 2020, Guildford, UK, September 14–18, 2020, Proceedings, Part I 25. Springer, 480–501.
  62. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023).
  63. Haiyong Wang and Kaixuan Guo. 2019. Byzantine Fault Tolerant Algorithm Based on Vote. In 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). 190–196. https://doi.org/10.1109/CyberC.2019.00041
  64. CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks. In International Conference on Machine Learning. PMLR, 36058–36076.
  65. Bloom: A 176b-parameter open-access multilingual language model. arXiv preprint arXiv:2211.05100 (2022).
  66. Doctorglm: Fine-tuning your chinese doctor is not a herculean task. arXiv preprint arXiv:2304.01097 (2023).
  67. Pipemare: Asynchronous pipeline parallel dnn training. Proceedings of Machine Learning and Systems 3 (2021), 269–296.
  68. Decentralized training of foundation models in heterogeneous environments. Advances in Neural Information Processing Systems 35 (2022), 25464–25477.
  69. {{\{{BatchCrypt}}\}}: Efficient homomorphic encryption for {{\{{Cross-Silo}}\}} federated learning. In 2020 USENIX annual technical conference (USENIX ATC 20). 493–506.
  70. Deep learning with elastic averaging SGD. Advances in neural information processing systems 28 (2015).
  71. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01068 (2022).
  72. idlg: Improved deep leakage from gradients. arXiv preprint arXiv:2001.02610 (2020).
  73. A survey of large language models. arXiv preprint arXiv:2303.18223 (2023).
  74. Byzantine-robust federated learning with optimal statistical rates. In International Conference on Artificial Intelligence and Statistics. PMLR, 3151–3178.
  75. Deep leakage from gradients. Advances in neural information processing systems 32 (2019).
Citations (1)
View on Semantic Scholar
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up

Summary

  • The paper presents a robust framework for decentralized LLM training that addresses security vulnerabilities introduced by pipeline parallelism.
  • It demonstrates how traditional federated learning security methods fall short due to altered data exchange structures and serial processing.
  • Experimental results validate improved resiliency and rapid recovery from hardware failures and malicious attacks in the proposed framework.

Introduction

Decentralization in the context of LLM training has become a prominent approach aiming to democratize access to this advanced AI technology. The recent exploration into this field probes the robustness of decentralized training frameworks, specifically when employing pipeline parallelism—a stark departure from the traditional federated learning (FL) schemas. The focus intensifies on the distinct security-related challenges this novel training strategy introduces, especially considering the intricacies of managing hardware faults, preserving data privacy, and mitigating malicious attacks. A keen observation reveals that security techniques flourishing in the FL domain may falter in the presence of pipeline parallelism, prompting a need for new, tailored solutions.

Background and Potential Threats

Understanding the potential threats to decentralized training is pivotal for fortifying these systems. Hardware malfunctions, while extensively discussed in terms of fault tolerance, have overshadowed the more subtle, yet equally crucial security risks, notably privacy inference and poisoning attacks. In decentralized training, the exchange of data during training, coupled with the open environment, amplifies the vulnerability to these attacks. Malicious entities could potentially exploit these systems to either reconstruct training data or introduce harmful alterations, jeopardizing the entire training process.

Limitations of Secure Aggregate in FL

When confronting challenges specific to decentralized training, security techniques borrowed from federated learning fall short. These methods, forged in the FL crucible, struggle within synergies dictated by pipeline parallelism for reasons twofold. Structurally, pipeline parallelism is essentially a serial progression, lacking the abundant comparable values necessary for techniques like secure aggregation to thrive. Additionally, decentralized training frameworks fundamentally modify both the object and frequency of data exchanges, rendering conventional FL security measures impractical.

Robust Decentralized Training

The journey towards resilient decentralized training frameworks begins with the formulation of robust components. The challenge lies in crafting defenses that effectively counter the identified threats while maintaining the delicate balance between security and the efficiency of the training process. Practical solutions consider fast recovery from hardware failures, detection of stage-level malicious behavior, and privacy preservation. Traditional methods are re-evaluated through a fresh lens, emphasizing the need to develop innovative strategies capable of sustaining the security of these systems without hampering their performance.

A Case Study

By employing a considered approach to attack detection and efficient training, a robust framework is presented that addresses the aforementioned threats. Demonstrating via experimental validation, the proposed strategies showcase a remarkable improvement in model robustness. This case paper not only exemplifies the susceptibility of standard decentralized training methodologies to attack but also solidifies the stance that comprehensive, specialized defense mechanisms are both necessary and effective for enhancing the security of decentralized LLM training.

Conclusion

This investigation into the robustness of decentralized LLM training spotlights a series of challenges and proposes strategic responses. Unveiling the intricacies and vulnerabilities inherent in pipeline parallelism, the discussion kindles necessary caution and primes the research community towards developing fortified decentralized strategies. As the future beckons for secure and democratized AI, this paper lays the groundwork, urging researchers to turn their gaze towards the security concerns that accompany the promising horizon of decentralized training frameworks.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets