Is 2024 the year of the Federated Learning ie "Year of Mobile ML"?
Rising data-privacy pressure and more capable phones make federated learning — training models on-device and sharing only parameters, never raw data — a plausible breakout for 2024. The harder questions are uneven hardware, battery and compute limits, and algorithms that learn well from decentralized data. Gesture-based ML, from emotion and intent recognition to rehab monitoring, is where on-device processing earns its keep.
In English, please
Federated learning trains an AI model using data spread across many phones, without ever moving that data off the phones. Each device learns from whatever data it locally holds and sends back only the small updates it learned -- not the raw data itself. A central server blends those updates from many devices into one improved shared model, then repeats the cycle, so the model keeps getting smarter while nobody's private information ever leaves their pocket.
Two things are pushing this technique toward the mainstream. First, rising concern about data privacy makes companies wary of collecting personal information the old-fashioned way. Second, phones have gotten powerful enough to actually do this on-device calculation themselves -- something that used to be less feasible. Big tech companies are now putting money into the approach, which could speed adoption along.
It isn't simple yet, though. Phones differ wildly in processing power and battery life, so some can contribute more training than others without a noticeable slowdown or drained battery. And building algorithms that learn well from scattered, inconsistent pockets of data -- rather than one tidy central pile -- is still a hard engineering problem.
As a proof point for how useful this kind of on-device AI could get, the piece walks through "gesture ML" -- software that reads hand movements and body cues -- paired with chatbot-style AI like GPT-4 to interpret what they mean. Five sketched uses: guessing someone's mood from how they move their hands, predicting what a person is about to select inside a virtual- or augmented-reality environment, letting a factory robot recognize a supervisor's hand signals, tracking a rehab patient's movements to measure recovery progress, and -- flagged as the most ethically fraught -- attempting to guess someone's age, gender, or ethnicity from their movement and speech patterns.
None of this is presented as settled. Whether 2024 turns out to be the breakout year for on-device, privacy-preserving AI is left as an open question, but the direction of travel -- driven by privacy pressure, growing phone processing power, and big-company investment -- points that way.
It is no secret that the increasing focus on data privacy and security, along with advancements in mobile technology, makes federated learning an attractive proposition. This approach allows mobile devices to contribute to machine learning models without sharing raw data, thus preserving user privacy. The growing computational power of mobile devices enables them to handle the local processing required for federated learning, making this more feasible than in the past. Before we continue, let’s solidify the lexicon.
What is Federated Learning?
Federated Learning is a machine learning approach that enables the training of algorithms across multiple decentralized devices or servers holding local data samples, without exchanging them. This method is particularly beneficial for preserving privacy and reducing the need to transfer large amounts of data. In federated learning, an algorithm is sent to each device, where it learns from the data present there. The device then sends back only the updated model parameters, not the data itself, to a central server. The server aggregates these updates from all devices to improve the overall model. This process iteratively continues, resulting in a robust and comprehensive model trained on diverse data sources, while significantly mitigating privacy and security concerns.
But there is more…
Additionally, major tech companies are investing in federated learning research and implementation, which could accelerate its adoption. However, there are challenges, including managing the variability in hardware capabilities across devices, ensuring efficient use of battery and computational resources, and developing robust algorithms that can learn effectively from decentralized data. If these challenges can be addressed effectively in the near future, federated learning could indeed become a significant trend in mobile technology in 2024, offering a new paradigm for privacy-conscious, decentralized machine learning.
How Gesture ML will play a pivotal role for on-device processing in 2024?
Gesture-based Machine Learning (ML) and Large Language Models (LLMs) like GPT-4 are increasingly being used for analyzing and predicting various aspects of human behavior and intent.
Here are five examples of how these models can be utilized, according to GPT-4:
- Emotion Recognition through Gestures: Gesture ML models can be trained on datasets of human gestures to predict emotional states. For example, a model might analyze the speed, rhythm, and amplitude of hand movements to infer whether a person is excited, nervous, or calm. This can be particularly useful in enhancing user experience in virtual reality or in providing emotional insights for mental health assessments.
- Intent Prediction in Human-Computer Interaction: LLMs and ML models can interpret human gestures in the context of human-computer interaction. For instance, a model could analyze hand movements or facial expressions to predict a user’s intent to select, move, or interact with virtual objects in a computer interface. This technology is increasingly relevant in the development of more intuitive and interactive VR and AR environments.
- Gesture-Based Control in Robotics: Robotics can employ gesture ML models to understand and respond to human gestures. For example, a robot in a manufacturing setting might be trained to recognize specific hand signals from a human supervisor to start, stop, or change functions. This improves safety and efficiency in environments where traditional forms of communication may be impractical.
- Health Monitoring and Rehabilitation: Gesture recognition models can play a significant role in health monitoring and physical rehabilitation. For instance, a model could analyze the movement patterns of a stroke patient to assess the progress of their recovery, or detect early signs of motor function decline in elderly patients, aiding in proactive healthcare.
- Demographic Inference: While more ethically and technically complex, some ML models attempt to infer demographics like age, gender, or ethnicity based on physical gestures and speech patterns. However, it’s crucial to note that such applications must be approached with extreme caution to avoid biases and respect privacy and ethical considerations.
These examples highlight the potential of Gesture ML and LLMs in various fields. However, it’s important to remember that the accuracy and appropriateness of these technologies depend on the quality of the data they’re trained on, and they must be developed and used with a strong commitment to ethical standards and privacy.
As of 11th of January 2024, it’s challenging to definitively predict whether full 2024 will be the year that federated learning becomes prevalent on native mobile devices, but the trend is certainly moving in that direction.
Do you agree?
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