Row 81760

Row ID: 81760 | Dataset Entry | Axioma AXP Content Repository

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Yeah, I feel I shouldn't have to be doing this much work to convince him, but here we are lol. My colleague (who is a literal, definitive genius) thinks that in federated learning, since we have no access to data, we have to build the centralized model with one-hot encoding and just keep a count of how many users we would have. So then we maintain a table on the users device with that number of columns and just update it every few months, but train on that dataset with all of those empty ohe columns. "It's a neural network, it will be fine", but he's saying that we HAVE to build the most basic version of the model possible, we can't generate any synthetic data or acquire any data to build a baseline estimate, because that is the point of federated learning.

I disagree with that, and feel a dynamically determined embedding layer:

based on one or multiple categorical variables that would include "user_id, as a way to distinguish one user from another and making the size of the layer dependent on range(user_id), so it scales properly on the patient device with 1 id, or the centralized model with the outputs from whatever number it ends up being....

would be enough to ensure each user has a chance at receiving a personalized recommendation that would improve over time.

Neither of us is willing to budge lol.

FieldValue
text Yeah, I feel I shouldn't have to be doing this much work to convince him, but here we are lol. My colleague (who is a literal, definitive genius) thinks that in federated learning, since we have no access to data, we have to build the centralized model with one-hot encoding and just keep a count of how many users we would have. So then we maintain a table on the users device with that number of columns and just update it every few months, but train on that dataset with all of those empty ohe col…
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-24
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  "text": "Yeah, I feel I shouldn't have to be doing this much work to convince him, but here we are lol. My colleague (who is a literal, definitive genius) thinks that in federated learning, since we have no access to data, we have to build the centralized model with one-hot encoding and just keep a count of how many users we would have. So then we maintain a table on the users device with that number of columns and just update it every few months, but train on that dataset with all of those empty ohe columns. \"It's a neural network, it will be fine\", but he's saying that we HAVE to build the most basic version of the model possible, we can't generate any synthetic data or acquire any data to build a baseline estimate, because that is the point of federated learning.\n\nI disagree with that, and feel a dynamically determined embedding layer:\n\nbased on one or multiple categorical variables that would include \"user_id, as a way to distinguish one user from another and making the size of the layer dependent on range(user_id), so it scales properly on the patient device with 1 id, or the centralized model with the outputs from whatever number it ends up being....\n\nwould be enough to ensure each user has a chance at receiving a personalized recommendation that would improve over time.\n\nNeither of us is willing to budge lol.",
  "label": "r/datascience",
  "dataType": "comment",
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