A list of puns related to "Federated Learning of Cohorts"
https://github.com/google/ads-privacy/blob/master/proposals/FLoC/FLOC-Whitepaper-Google.pdf
Reading through the above Paper gave me a bit of a headache. I understand how you can group people into Cohorts of people who read about reggae vs heavy metal and show them ads based on their membership to those groups without blasting out personal identifiers every time they browse the internet.
What I don't understand is will google be giving us the response variable to optimize towards? So will I know that people who read about heavy metal have gone on to buy my client's products or sign up signup for their newsletter? In the above whitepaper, they seem to say that recall went up and cohorts were predictive of conversions.
Also, does anyone know about how the big exchanges feel about these types of proposals? If this is the future of targeting it seems AdX will be the only game in town.
Smart healthcare services can be provided by using Internet of Things (IoT) technologies that monitor the health conditions of patients and their vital body parameters. The majority of IoT solutions used to enable such services are wearable devices, such as smartwatches, ECG monitors, and blood pressure monitors. The huge amount of data collected from smart medical devices leads to major security and privacy issues in the IoT domain. Considering Remote Patient Monitoring (RPM) applications, we will focus on Anomaly Detection (AD) models, whose purpose is to identify events that differ from the typical user behavior patterns. Generally, while designing centralized AD models, the researchers face security and privacy challenges (e.g., patient data privacy, training data poisoning).
To overcome these issues, the researchers of this paper propose an Anomaly Detection (AD) model based on Federated Learning (FL). Federated Learning (FL) allows different devices to collaborate and perform training locally in order to build Anomaly Detection (AD) models without sharing patients’ data. Specifically, the researchers propose a hierarchical Federated Learning (FL) that enables collaboration among different organizations, by building various Anomaly Detection (AD) models for patients with similar health conditions.
Continue Reading the Paper Summary: https://www.marktechpost.com/2022/01/01/hierarchical-federated-learning-based-anomaly-detection-using-digital-twins-for-internet-of-medical-things-iomt/
Full Paper: https://arxiv.org/pdf/2111.12241.pdf
Introduced a few years ago by Google, Federated learning is an approach that downloads the current model and computes an updated model on the device itself (a little like edge computing) using local data. Updates from these locally trained models are then sent from the devices back to the central server where they are aggregated. Essentially, weights are averaged and then a single consolidated and improved global model is sent back to the devices.
This allows multiple organizations to collaborate on the development of models, exposing the model to a significantly wider range of data than what any single organization possesses in-house, while preserving data security - as only updates are shared with devices - not the actual data.
Original Article - https://blog.mindkosh.com/what-is-federated-learning/
Smart Text Selection is one of Android’s most popular features, assisting users in selecting, copying, and using text by anticipating the desired word or combination of words around a user’s tap and expanding the selection appropriately. Selections are automatically extended with this feature, and users are offered an app to open selections with defined classification categories, such as addresses and phone numbers, saving them even more time.
The Google team made efforts to improve the performance of Smart Text Selection by utilizing federated learning to train a neural network model responsible for user interactions while maintaining personal privacy. The research team was able to enhance the model’s selection accuracy by up to 20% on some sorts of entities thanks to this effort, which is part of Android’s new Private Compute Core safe environment.
The model is trained to only select a single word to reduce the incidence of making multi-word selections in error. The Smart Text Selection feature was first trained on proxy data derived from web pages that had schema.org annotations attached to them. While this method of training on schema.org annotations was effective, it had a number of drawbacks. The data was not at all like the text users viewed on their devices.
Google Blog: https://ai.googleblog.com/2021/11/predicting-text-selections-with.html
AI is a complex and constantly advancing technology. What's your favorite use case for Federated Learning? What about Artificial Intelligence? Here are a few that Phoenix Global is aiming for https://www.cryptopolitan.com/applications-of-federated-learning-and-artificial-intelligence/
Bringing that to blockchain via Federated Learning is a primary goal of Phoenix Global 💪
Federated learning is a new way to train artificial intelligence models with data from multiple sources while maintaining anonymity. This removes many barriers and opens up the possibility for even more sharing in machine learning research.
The latest results published in Nature Medicine show promising new research wherein the federated learning models build powerful AI models that can be generalized among healthcare institutions. These findings are currently for the healthcare industry. It shows that further down the line, it could have a significant role in energy, financial services, and manufacturing applications. Given the pandemic, healthcare institutions decided to take matters into their own hands and work together and found out that institutions in any industry can develop predictive AI models, and collaboration amongst professionals could set new standards in the domain of both accuracy and generalizability, the two factors that usually do not work together.
https://preview.redd.it/b33hgnyul0p71.jpg?width=640&format=pjpg&auto=webp&s=b2045eb69bc3d61f94c516d942f9fd6c97b55923
In the process of data value release, GoodData realizes data privacy protection and secure sharing through the combination of various technologies. In the last article, we talked about the important role that differential privacy plays in preventing data disclosure. Now, we will introduce a technology applied by GoodData to assist multiple participants in machine learning on the premise of ensuring data privacy and security: federated learning.
The concept of federated learning
Federated learning is a machine learning technology that can train algorithms between multiple distributed edge devices or servers with local data samples without exchanging data samples. Participants do not need to transfer data to the server, but instead to the local training model. It only needs to transfer parameters between the server and each node, which solves the problem of data privacy.
According to the different data distribution among multiple data owners, federated learning can be divided into three categories: horizontal federated learning, vertical federated learning, and federated transfer learning.
Horizontal federated learning refers to the joint learning of participants when there is more overlap of sample features but less overlap of users. For example, banks A and B in different regions have similar businesses, but different users. With the cooperation of a third party (such as GoodData), the system aligns the encrypted samples of the data of A and B, selects samples with the same characteristics but different users, and then jointly trains a machine learning model in GoodData. In this process, participants' data are trained in an encrypted environment. Data privacy protection is guaranteed.
Vertically federated learning aims at joint learning among multi-party data owners with less sample feature overlap but more user overlap. For example, hospital A and bank B in the same region have data from users in the region. Due to different businesses, the sample special diagnosis is also different. Through vertical federated learning, both A and B can jointly improve the model effect on the premise of data protection, and will not lose their original data.
Federated transfer learning is applicable where there is
... keep reading on reddit ➡Ever wondered how your mobile keyboard gives you the next word suggestions? How do they give personalised suggestions, while at the same time ensuring the privacy of individuals?
Check out my blog post "Federated Learning for Mobile Keyboard Prediction", which talks about how this happens, in a privacy-preserving manner.
Blog Post - PPML Series #3 - Federated Learning for Mobile Keyboard Prediction
Annotated Paper - Annotated-ML-Papers/Federated Learning for Mobile Keyboard Prediction
Federated learning is a machine learning technique in which an algorithm is trained across numerous decentralized edge devices or servers, keeping local data samples without being exchanged. This prevents the collecting of personally identifiable information. It is frequently accomplished by learning a single global model for all users, although their data distributions may differ. Due to this variability, an algorithm that can personalize a global model for each user has been developed.
However, privacy concerns may prevent a truly global model from being learned in some cases. While sending user embedding updates to a central server may reveal the preferences encoded in the embeddings, it is required to train a completely global federated model. Even if models do not include user-specific embeddings, having some parameters local to user devices reduces server-client communication and allows for responsible personalization of those parameters for each user.
Google AI introduces an approach that enables scalable partially local federated learning in their work “Federated Reconstruction: Partially Local Federated Learning”. Some model parameters are never aggregated on the server in this approach. This strategy trains a piece of the model to be personal for each user while eliminating transmission of these parameters for models other than Matrix Factorization. In the case of matrix factorization, a recommender model is trained. The model retains user embeddings local to each user service.
Paper: https://arxiv.org/pdf/2102.03448.pdf
Github: https://github.com/google-research/federated/tree/master/reconstruction
Phoenix Global aims to be a leader and champion in the Federated Learning and IoT space. Federated Learning can be though of as distributed Artificial Learning/Machine Learning and IoT facilitates the connection of billions of network-enabled things.
Learn more on how Phoenix Global looks to build this into the blockchain.
https://www.cryptopolitan.com/the-power-of-federated-learning-internet-of-things/
Hi all, I am currently researching Federated learning on TinyML. I would like to know the minimum amount of devices you would suggest I have for researching. I'm currently working with 2 devices. Would it suffice? If not, would you suggest I emulate a few raspberry Pis? Or purchase a few extras?
Federated Learning (FL) is a distributed learning paradigm that can learn a global or a personalized model for each user relying on decentralized data provided by edge devices. Since these edge devices do not need to share any data, FL can handle privacy issues that make centralized solutions unusable in specific domains (e.g., medical). You can think about a machine learning model for facial recognition. A centralized approach requires uploading the local data of each user externally (e.g., on a server), a solution that cannot ensure data privacy.
Considering FL in the Computer Vision (CV) domain, currently, only image classification in small-scale datasets and models has been evaluated, while most of the recent works focus on large-scale supervised/self-supervised pre-training models based on CNN or Transformers. At the moment, the research community lacks a library that connects different CV tasks with FL algorithms. For this reason, the researchers of this paper designed FedCV, a unified federated learning library that connects various FL algorithms with multiple important CV tasks, including image segmentation and object detection. To lighten the effort of CV researchers, FedCV provides representative FL algorithms through easy-to-use APIs. Moreover, the framework is flexible in exploring new protocols of distributed computing (e.g., customizing the exchange information among clients) and defining specialized training procedures.
💪 Federated Learning Consortium 💪
Phoenix Global will be a key part of a Global Federated Learning Consortium that will include industry experts from the likes of Tencent and Ant Financial. The consortium will be the first of its kind in the world and serve to be the leading thought leadership and ecosystem authority in Federated Learning. An official announcement of the consortium is expected in early August (currently details unrevealed).
Stay tuned for more 🤜🤛
🚨Like & retweet 🚨 https://twitter.com/Phoenix_Chain/status/1421123016931651593?s=20
Hi!
Does anyone know of any in-detail descriptions/surveys of FL deployments in practice? What type of aggregations do people use and how they ensure privacy? Do most deployments rely on tf-federated?
I tried googling around, but am struggling to find much information.
Thanks a lot!
Smart healthcare services can be provided by using Internet of Things (IoT) technologies that monitor the health conditions of patients and their vital body parameters. The majority of IoT solutions used to enable such services are wearable devices, such as smartwatches, ECG monitors, and blood pressure monitors. The huge amount of data collected from smart medical devices leads to major security and privacy issues in the IoT domain. Considering Remote Patient Monitoring (RPM) applications, we will focus on Anomaly Detection (AD) models, whose purpose is to identify events that differ from the typical user behavior patterns. Generally, while designing centralized AD models, the researchers face security and privacy challenges (e.g., patient data privacy, training data poisoning).
To overcome these issues, the researchers of this paper propose an Anomaly Detection (AD) model based on Federated Learning (FL). Federated Learning (FL) allows different devices to collaborate and perform training locally in order to build Anomaly Detection (AD) models without sharing patients’ data. Specifically, the researchers propose a hierarchical Federated Learning (FL) that enables collaboration among different organizations, by building various Anomaly Detection (AD) models for patients with similar health conditions.
Continue Reading the Paper Summary: https://www.marktechpost.com/2022/01/01/hierarchical-federated-learning-based-anomaly-detection-using-digital-twins-for-internet-of-medical-things-iomt/
Full Paper: https://arxiv.org/pdf/2111.12241.pdf
Introduced a few years ago by Google, Federated learning is an approach that downloads the current model and computes an updated model on the device itself (a little like edge computing) using local data. These locally trained models are then sent from the devices back to the central server where they are aggregated. Essentially, weights are averaged and then a single consolidated and improved global model is sent back to the devices.
This allows multiple organizations to collaborate on the development of models, exposing the model to a significantly wider range of data than what any single organization possesses in-house, while preserving data security - as only updates are shared with devices - not the actual data.
Original Article - https://blog.mindkosh.com/what-is-federated-learning/
Federated learning is a new way to train artificial intelligence models with data from multiple sources while maintaining anonymity. This removes many barriers and opens up the possibility for even more sharing in machine learning research.
The latest results published in Nature Medicine show promising new research wherein the federated learning models build powerful AI models that can be generalized among healthcare institutions. These findings are currently for the healthcare industry. It shows that further down the line, it could have a significant role in energy, financial services, and manufacturing applications. Given the pandemic, healthcare institutions decided to take matters into their own hands and work together and found out that institutions in any industry can develop predictive AI models, and collaboration amongst professionals could set new standards in the domain of both accuracy and generalizability, the two factors that usually do not work together.
Federated learning is a machine learning technique in which an algorithm is trained across numerous decentralized edge devices or servers, keeping local data samples without being exchanged. This prevents the collecting of personally identifiable information. It is frequently accomplished by learning a single global model for all users, although their data distributions may differ. Due to this variability, an algorithm that can personalize a global model for each user has been developed.
However, privacy concerns may prevent a truly global model from being learned in some cases. While sending user embedding updates to a central server may reveal the preferences encoded in the embeddings, it is required to train a completely global federated model. Even if models do not include user-specific embeddings, having some parameters local to user devices reduces server-client communication and allows for responsible personalization of those parameters for each user.
Google AI introduces an approach that enables scalable partially local federated learning in their work “Federated Reconstruction: Partially Local Federated Learning”. Some model parameters are never aggregated on the server in this approach. This strategy trains a piece of the model to be personal for each user while eliminating transmission of these parameters for models other than Matrix Factorization. In the case of matrix factorization, a recommender model is trained. The model retains user embeddings local to each user service.
Paper: https://arxiv.org/pdf/2102.03448.pdf
Github: https://github.com/google-research/federated/tree/master/reconstruction
Ever wondered how your mobile keyboard gives you the next word suggestions? How do they give personalised suggestions, while at the same time ensuring the privacy of individuals?
Check out my blog post "Federated Learning for Mobile Keyboard Prediction", which talks about how this happens, in a privacy-preserving manner.
Blog Post - PPML Series #3 - Federated Learning for Mobile Keyboard Prediction
Annotated Paper - Annotated-ML-Papers/Federated Learning for Mobile Keyboard Prediction
What do you think about federated learning for users' privacy-preserving?
Federated Learning (FL) is a distributed learning paradigm that can learn a global or a personalized model for each user relying on decentralized data provided by edge devices. Since these edge devices do not need to share any data, FL can handle privacy issues that make centralized solutions unusable in specific domains (e.g., medical). You can think about a machine learning model for facial recognition. A centralized approach requires uploading the local data of each user externally (e.g., on a server), a solution that cannot ensure data privacy.
Considering FL in the Computer Vision (CV) domain, currently, only image classification in small-scale datasets and models has been evaluated, while most of the recent works focus on large-scale supervised/self-supervised pre-training models based on CNN or Transformers. At the moment, the research community lacks a library that connects different CV tasks with FL algorithms. For this reason, the researchers of this paper designed FedCV, a unified federated learning library that connects various FL algorithms with multiple important CV tasks, including image segmentation and object detection. To lighten the effort of CV researchers, FedCV provides representative FL algorithms through easy-to-use APIs. Moreover, the framework is flexible in exploring new protocols of distributed computing (e.g., customizing the exchange information among clients) and defining specialized training procedures.
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