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Keywords {🔍}

training, dpsgd, sign, private, model, features, techniques, projects, issue, sparsity, navigation, open, pull, requests, actions, security, sparse, lufe, privacy, make, case, aware, research, topic, considered, csharrison, github, labels, type, milestone, footer, skip, content, menu, product, solutions, resources, source, enterprise, pricing, search, jump, wicg, turtledove, public, notifications, fork, star, code, issues,

Topics {✒️}

private model training protected audience api comment metadata assignees type projects projects milestone privacy considerations training loop categorical features dp-sgd recently mentioned include dp gradients calculated rendering impossible embedding tables explainer linked org/abs/2404 exploring supporting specific approaches assigned labels labels type milestone relationships research phase github preserve sparsity optimization techniques features research sparsity techniques skip jump sign cases make destroys backprop rely case working llms aware topic remedy clear considered context face thoughts feedback concern

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DiscussionForumPosting:
      context:https://schema.org
      headline:Private model training (DP-SGD) with sparse features
      articleBody:Hello, Private model training has been recently mentioned [here](https://github.com/WICG/turtledove/blob/main/PA_private_model_training.md). One of the privacy considerations is to include DP in the training loop through DP-SGD. There are cases when DP-SGD would make the training process considerably slower as it destroys the sparsity of the gradients calculated during backprop, rendering impossible to use optimization techniques that rely on such sparsity. This is usually the case when some features are categorical features or working with embedding tables in the case of LLMs. I am aware there is research around this topic to remedy it, although it is not clear from the explainer linked above if this is something that has been considered in the context of Protected Audience API. Are there any techniques that are being considered to face this or thoughts about this topic? Thanks
      author:
         url:https://github.com/Lufe44
         type:Person
         name:Lufe44
      datePublished:2024-12-17T23:53:36.000Z
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      url:https://github.com/1370/turtledove/issues/1370
      context:https://schema.org
      headline:Private model training (DP-SGD) with sparse features
      articleBody:Hello, Private model training has been recently mentioned [here](https://github.com/WICG/turtledove/blob/main/PA_private_model_training.md). One of the privacy considerations is to include DP in the training loop through DP-SGD. There are cases when DP-SGD would make the training process considerably slower as it destroys the sparsity of the gradients calculated during backprop, rendering impossible to use optimization techniques that rely on such sparsity. This is usually the case when some features are categorical features or working with embedding tables in the case of LLMs. I am aware there is research around this topic to remedy it, although it is not clear from the explainer linked above if this is something that has been considered in the context of Protected Audience API. Are there any techniques that are being considered to face this or thoughts about this topic? Thanks
      author:
         url:https://github.com/Lufe44
         type:Person
         name:Lufe44
      datePublished:2024-12-17T23:53:36.000Z
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      url:https://github.com/1370/turtledove/issues/1370
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