A few years ago, I wondered how edge data would ever be useful given the enormous cost of transmitting all the data to either the centralized data center or some variant of cloud infrastructure. (It is said that 5G will solve that problem).
Consider, for example, applications of vast sensor networks that stream a great deal of data at small intervals. Vehicles on the move are a good example.
There is telemetry from cameras, radar, sonar, GPS and LIDAR, the latter about 70MB/sec. This could quickly amount to four terabytes per day (per vehicle). How much of this data needs to be retained? Answers I heard a few years ago were along two lines:
My counterarguments at the time were:
Introducing TensorFlow federated, via The TensorFlow Blog:
This centralized approach can be problematic if the data is sensitive or expensive to centralize. Wouldn't it be better if we could run the data analysis and machine learning right on the devices where that data is generated, and still be able to aggregate together what's been learned?
Since I looked at this a few years ago, the distinction between an edge device and a sensor has more or less disappeared. Sensors can transmit via wifi (though there is an issue of battery life, and if they're remote, that's a problem); the definition of the edge has widened quite a bit.
Decentralized data collection and processing have become more powerful and able to do an impressive amount of computing. The case is point in Intel's Introducing the Intel Neural Compute Stick 2 computer vision and deep learning accelerator powered by the Intel Movidius Myriad X VPU, that can stick into a Pi for less than $70.00.
But for truly distributed processing, the Apple A13 chipset in the iPhone 11 has a few features that boggle the mind: From Inside Apple's A13 Bionic system-on-chip Neural Engine, a custom block of silicon separate from the CPU and GPU, focused on accelerating Machine Learning computations. The CPU has a set of "machine learning accelerators" that perform matrix multiplication operations up to six times faster than the CPU alone. It's not clear how exactly this hardware is accessed, but for tasks like machine learning (ML) that use lots of matrix operations, the CPU is a powerhouse. Note that this matrix multiplication hardware is part of the CPU cores and separate from the Neural Engine hardware.
This should beg the question, "Why would a smartphone have neural net and machine learning capabilities, and does that have anything to do with the data transmission problem for the edge?" A few years ago, I thought the idea wasn't feasible, but the capability of distributed devices has accelerated. How far-fetched is this?
Let's roll the clock back thirty years. The finance department of a large diversified organization would prepare in the fall a package of spreadsheets for every part of the organization that had budget authority. The sheets would start with low-level detail, official assumptions, etc. until they all rolled up to a small number of summary sheets that were submitted headquarters. This was a terrible, cumbersome way of doing things, but it does, in a way, presage the concept of federated learning.
Another idea that vanished is Push Technology that shared the same network load as centralizing sensor data, just in the opposite direction. About twenty-five years, when everyone had a networked PC on their desk, the PointCast Network used push technology. Still, it did not perform as well as expected, often believed to be because its traffic burdened corporate networks with excessive bandwidth use, and was banned in many places. If Federated Learning works, those problems have to be addressed
Though this estimate changes every day, there are 3 billion smartphones in the world and 7 billion connected devices.You can almost hear the buzz in the air of all of that data that is always flying around. The canonical image of ML is that all of that data needs to find a home somewhere so that algorithms can crunch through it to yield insights. There are a few problems with this, especially if the data is coming from personal devices, such as smartphones, Fitbit's, even smart homes.
Moving highly personal data across the network raises privacy issues. It is also costly to centralize this data at scale. Storage in the cloud is asymptotically approaching zero in cost, but the transmission costs are not. That includes both local WiFi from the devices (or even cellular) and the long-distance transmission from the local collectors to the central repository. This s all very expensive at this scale.
Suppose, large-scale AI training could be done on each device, bringing the algorithm to the data, rather than vice-versa? It would be possible for each device to contribute to a broader application while not having to send their data over the network. This idea has become respectable enough that it has a name - Federated Learning.
Jumping ahead, there is no controversy that training a network without compromising device performance and user experience, or compressing a model and resorting to a lower accuracy are not alternatives. In Federated Learning: The Future of Distributed Machine Learning:
To train a machine learning model, traditional machine learning adopts a centralized approach that requires the training data to be aggregated on a single machine or in a datacenter. This is practically what giant AI companies such as Google, Facebook, and Amazon have been doing over the years. This centralized training approach, however, is privacy-intrusive, especially for mobile phone usersTo train or obtain a better machine learning model under such a centralized training approach, mobile phone users have to trade their privacy by sending their personal data stored inside phones to the clouds owned by the AI companies.
The federated learning approach decentralizes training across mobile phones dispersed across geography. The presumption is that they collaboratively develop machine learning while keeping their personal data on their phones. For example, building a general-purpose recommendation engine for music listeners. While the personal data and personal information are retained on the phone, I am not at all comfortable that data contained in the result sent to the collector cannot be reverse-engineered - and I havent heard a convincing argument to the contrary.
Here is how it works. A computing group, for example, is a collection of mobile devices that have opted to be part of a large scale AI program. The device is "pushed" a model and executes it locally and learns as the model processes the data. There are some alternatives to this. Homogeneous models imply that every device is working with the same schema of data. Alternatively, there are heterogeneous models where harmonization of the data happens in the cloud.
Here are some questions in my mind.
Here is the fuzzy part: federated learning sends the results of the learning as well as some operational detail such as model parameters and corresponding weights back to the cloud. How does it do that and preserve your privacy and not clog up your network? The answer is that the results are a fraction of the data, and since the data itself is not more than a few Gb, that seems plausible. The results sent to the cloud can be encrypted with, for example, homomorphic encryption (HE). An alternative is to send the data as a tensor, which is not encrypted because it is not understandable by anything but the algorithm. The update is then aggregated with other user updates to improve the shared model. Most importantly, all the training data remains on the user's devices.
In CDO Review, The Future of AI. May Be In Federated Learning:
Federated Learning allows for faster deployment and testing of smarter models, lower latency, and less power consumption, all while ensuring privacy. Also, in addition to providing an update to the shared model, the improved (local) model on your phone can be used immediately, powering experiences personalized by the way you use your phone.
There is a lot more to say about this. The privacy claims are a little hard to believe. When an algorithm is pushed to your phone, it is easy to imagine how this can backfire. Even the tensor representation can create a problem. Indirect reference to real data may be secure, but patterns across an extensive collection can surely emerge.
Read more here:
Federated machine learning is coming - here's the questions we should be asking - Diginomica
- Are We Overly Infatuated With Deep Learning? - Forbes [Last Updated On: August 18th, 2024] [Originally Added On: December 28th, 2019]
- CMSWire's Top 10 AI and Machine Learning Articles of 2019 - CMSWire [Last Updated On: August 18th, 2024] [Originally Added On: December 28th, 2019]
- Can machine learning take over the role of investors? - TechHQ [Last Updated On: August 18th, 2024] [Originally Added On: December 28th, 2019]
- Pear Therapeutics Expands Pipeline with Machine Learning, Digital Therapeutic and Digital Biomarker Technologies - Business Wire [Last Updated On: August 18th, 2024] [Originally Added On: January 11th, 2020]
- Dell's Latitude 9510 shakes up corporate laptops with 5G, machine learning, and thin bezels - PCWorld [Last Updated On: August 18th, 2024] [Originally Added On: January 11th, 2020]
- Limits of machine learning - Deccan Herald [Last Updated On: August 18th, 2024] [Originally Added On: January 11th, 2020]
- Forget Machine Learning, Constraint Solvers are What the Enterprise Needs - - RTInsights [Last Updated On: August 18th, 2024] [Originally Added On: January 11th, 2020]
- Tiny Machine Learning On The Attiny85 - Hackaday [Last Updated On: August 18th, 2024] [Originally Added On: January 11th, 2020]
- Finally, a good use for AI: Machine-learning tool guesstimates how well your code will run on a CPU core - The Register [Last Updated On: August 18th, 2024] [Originally Added On: January 11th, 2020]
- How Will Your Hotel Property Use Machine Learning in 2020 and Beyond? | - Hotel Technology News [Last Updated On: August 18th, 2024] [Originally Added On: January 11th, 2020]
- Technology Trends to Keep an Eye on in 2020 - Built In Chicago [Last Updated On: August 18th, 2024] [Originally Added On: January 11th, 2020]
- AI and machine learning trends to look toward in 2020 - Healthcare IT News [Last Updated On: August 18th, 2024] [Originally Added On: January 11th, 2020]
- The 4 Hottest Trends in Data Science for 2020 - Machine Learning Times - machine learning & data science news - The Predictive Analytics Times [Last Updated On: August 18th, 2024] [Originally Added On: January 11th, 2020]
- The Problem with Hiring Algorithms - Machine Learning Times - machine learning & data science news - The Predictive Analytics Times [Last Updated On: August 18th, 2024] [Originally Added On: January 11th, 2020]
- Going Beyond Machine Learning To Machine Reasoning - Forbes [Last Updated On: August 18th, 2024] [Originally Added On: January 11th, 2020]
- Doctor's Hospital focused on incorporation of AI and machine learning - EyeWitness News [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Being human in the age of Artificial Intelligence - Deccan Herald [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Raleys Drive To Be Different Gets an Assist From Machine Learning - Winsight Grocery Business [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Break into the field of AI and Machine Learning with the help of this training - Boing Boing [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- BlackBerry combines AI and machine learning to create connected fleet security solution - Fleet Owner [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- What is the role of machine learning in industry? - Engineer Live [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Seton Hall Announces New Courses in Text Mining and Machine Learning - Seton Hall University News & Events [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Christiana Care offers tips to 'personalize the black box' of machine learning - Healthcare IT News [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Leveraging AI and Machine Learning to Advance Interoperability in Healthcare - - HIT Consultant [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Essential AI & Machine Learning Certification Training Bundle Is Available For A Limited Time 93% Discount Offer Avail Now - Wccftech [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Educate Yourself on Machine Learning at this Las Vegas Event - Small Business Trends [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- 2020: The year of seeing clearly on AI and machine learning - ZDNet [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- How machine learning and automation can modernize the network edge - SiliconANGLE [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Five Reasons to Go to Machine Learning Week 2020 - Machine Learning Times - machine learning & data science news - The Predictive Analytics Times [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Don't want a robot stealing your job? Take a course on AI and machine learning. - Mashable [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Adventures With Artificial Intelligence and Machine Learning - Toolbox [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Optimising Utilisation Forecasting with AI and Machine Learning - Gigabit Magazine - Technology News, Magazine and Website [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Machine Learning: Higher Performance Analytics for Lower ... [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Machine Learning Definition [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Machine Learning Market Size Worth $96.7 Billion by 2025 ... [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Difference between AI, Machine Learning and Deep Learning [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Machine Learning in Human Resources Applications and ... [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Pricing - Machine Learning | Microsoft Azure [Last Updated On: August 18th, 2024] [Originally Added On: January 19th, 2020]
- Looking at the most significant benefits of machine learning for software testing - The Burn-In [Last Updated On: August 18th, 2024] [Originally Added On: January 22nd, 2020]
- New York Institute of Finance and Google Cloud Launch A Machine Learning for Trading Specialization on Coursera - PR Web [Last Updated On: August 18th, 2024] [Originally Added On: January 22nd, 2020]
- Uncover the Possibilities of AI and Machine Learning With This Bundle - Interesting Engineering [Last Updated On: August 18th, 2024] [Originally Added On: January 22nd, 2020]
- Red Hat Survey Shows Hybrid Cloud, AI and Machine Learning are the Focus of Enterprises - Computer Business Review [Last Updated On: August 18th, 2024] [Originally Added On: January 22nd, 2020]
- Machine learning - Wikipedia [Last Updated On: August 18th, 2024] [Originally Added On: January 22nd, 2020]
- Vectorspace AI Datasets are Now Available to Power Machine Learning (ML) and Artificial Intelligence (AI) Systems in Collaboration with Elastic -... [Last Updated On: August 18th, 2024] [Originally Added On: January 22nd, 2020]
- Learning that Targets Millennial and Generation Z - HR Exchange Network [Last Updated On: August 18th, 2024] [Originally Added On: January 23rd, 2020]
- Machine learning and eco-consciousness key business trends in 2020 - Finfeed [Last Updated On: August 18th, 2024] [Originally Added On: January 24th, 2020]
- Jenkins Creator Launches Startup To Speed Software Testing with Machine Learning -- ADTmag - ADT Magazine [Last Updated On: August 18th, 2024] [Originally Added On: January 24th, 2020]
- Research report investigates the Global Machine Learning In Finance Market 2019-2025 - WhaTech Technology and Markets News [Last Updated On: August 18th, 2024] [Originally Added On: January 25th, 2020]
- Expert: Don't overlook security in rush to adopt AI - The Winchester Star [Last Updated On: August 18th, 2024] [Originally Added On: January 25th, 2020]
- I Know Some Algorithms Are Biased--because I Created One - Scientific American [Last Updated On: August 18th, 2024] [Originally Added On: February 1st, 2020]
- Iguazio Deployed by Payoneer to Prevent Fraud with Real-time Machine Learning - Business Wire [Last Updated On: August 18th, 2024] [Originally Added On: February 1st, 2020]
- Want To Be AI-First? You Need To Be Data-First. - Forbes [Last Updated On: August 18th, 2024] [Originally Added On: February 1st, 2020]
- How Machine Learning Will Lead to Better Maps - Popular Mechanics [Last Updated On: August 18th, 2024] [Originally Added On: February 1st, 2020]
- Technologies of the future, but where are AI and ML headed to? - YourStory [Last Updated On: August 18th, 2024] [Originally Added On: February 1st, 2020]
- In Coronavirus Response, AI is Becoming a Useful Tool in a Global Outbreak - Machine Learning Times - machine learning & data science news - The... [Last Updated On: August 18th, 2024] [Originally Added On: February 1st, 2020]
- This tech firm used AI & machine learning to predict Coronavirus outbreak; warned people about danger zones - Economic Times [Last Updated On: August 18th, 2024] [Originally Added On: February 1st, 2020]
- 3 books to get started on data science and machine learning - TechTalks [Last Updated On: August 18th, 2024] [Originally Added On: February 1st, 2020]
- JP Morgan expands dive into machine learning with new London research centre - The TRADE News [Last Updated On: August 18th, 2024] [Originally Added On: February 1st, 2020]
- Euro machine learning startup plans NYC rental platform, the punch list goes digital & other proptech news - The Real Deal [Last Updated On: August 18th, 2024] [Originally Added On: February 1st, 2020]
- The ML Times Is Growing A Letter from the New Editor in Chief - Machine Learning Times - machine learning & data science news - The Predictive... [Last Updated On: August 18th, 2024] [Originally Added On: February 1st, 2020]
- Top Machine Learning Services in the Cloud - Datamation [Last Updated On: August 18th, 2024] [Originally Added On: February 1st, 2020]
- Combating the coronavirus with Twitter, data mining, and machine learning - TechRepublic [Last Updated On: August 18th, 2024] [Originally Added On: February 1st, 2020]
- Itiviti Partners With AI Innovator Imandra to Integrate Machine Learning Into Client Onboarding and Testing Tools - PRNewswire [Last Updated On: August 18th, 2024] [Originally Added On: February 2nd, 2020]
- Iguazio Deployed by Payoneer to Prevent Fraud with Real-time Machine Learning - Yahoo Finance [Last Updated On: August 18th, 2024] [Originally Added On: February 2nd, 2020]
- ScoreSense Leverages Machine Learning to Take Its Customer Experience to the Next Level - Yahoo Finance [Last Updated On: August 18th, 2024] [Originally Added On: February 2nd, 2020]
- How Machine Learning Is Changing The Future Of Fiber Optics - DesignNews [Last Updated On: August 18th, 2024] [Originally Added On: February 2nd, 2020]
- How to handle the unexpected in conversational AI - ITProPortal [Last Updated On: August 18th, 2024] [Originally Added On: February 5th, 2020]
- SwRI, SMU fund SPARKS program to explore collaborative research and apply machine learning to industry problems - TechStartups.com [Last Updated On: August 18th, 2024] [Originally Added On: February 5th, 2020]
- Reinforcement Learning (RL) Market Report & Framework, 2020: An Introduction to the Technology - Yahoo Finance [Last Updated On: August 18th, 2024] [Originally Added On: February 5th, 2020]
- ValleyML Is Launching a Series of 3 Unique AI Expo Events Focused on Hardware, Enterprise and Robotics in Silicon Valley - AiThority [Last Updated On: August 18th, 2024] [Originally Added On: February 5th, 2020]
- REPLY: European Central Bank Explores the Possibilities of Machine Learning With a Coding Marathon Organised by Reply - Business Wire [Last Updated On: August 18th, 2024] [Originally Added On: February 5th, 2020]
- VUniverse Named One of Five Finalists for SXSW Innovation Awards: AI & Machine Learning Category - PRNewswire [Last Updated On: August 18th, 2024] [Originally Added On: February 5th, 2020]
- AI, machine learning, robots, and marketing tech coming to a store near you - TechRepublic [Last Updated On: August 18th, 2024] [Originally Added On: February 5th, 2020]
- Putting the Humanity Back Into Technology: 10 Skills to Future Proof Your Career - HR Technologist [Last Updated On: August 18th, 2024] [Originally Added On: February 6th, 2020]
- Twitter says AI tweet recommendations helped it add millions of users - The Verge [Last Updated On: August 18th, 2024] [Originally Added On: February 6th, 2020]
- Artnome Wants to Predict the Price of a Masterpiece. The Problem? There's Only One. - Built In [Last Updated On: August 18th, 2024] [Originally Added On: February 6th, 2020]
- Machine Learning Patentability in 2019: 5 Cases Analyzed and Lessons Learned Part 1 - Lexology [Last Updated On: August 18th, 2024] [Originally Added On: February 6th, 2020]
- The 17 Best AI and Machine Learning TED Talks for Practitioners - Solutions Review [Last Updated On: August 18th, 2024] [Originally Added On: February 6th, 2020]
- Overview of causal inference in machine learning - Ericsson [Last Updated On: August 18th, 2024] [Originally Added On: February 6th, 2020]
- Manchester Digital unveils 72% growth for digital businesses in the region - Education Technology [Last Updated On: August 18th, 2024] [Originally Added On: February 13th, 2020]