Science & Technology
Collaborative machine learning that preserves privacy
Researchers increase the accuracy and efficiency of a machine-learning method that safeguards user data

Written by Adam Zewe, MIT News Office
Training a machine-learning model to effectively perform a task, such as image classification, involves showing the model thousands, millions, or even billions of example images. Gathering such enormous datasets can be especially challenging when privacy is a concern, such as with medical images. Researchers from MIT and the MIT-born startup DynamoFL have now taken one popular solution to this problem, known as federated learning, and made it faster and more accurate.
Federated learning is a collaborative method for training a machine-learning model that keeps sensitive user data private. Hundreds or thousands of users each train their own model using their own data on their own device. Then users transfer their models to a central server, which combines them to come up with a better model that it sends back to all users.
A collection of hospitals located around the world, for example, could use this method to train a machine-learning model that identifies brain tumors in medical images, while keeping patient data secure on their local servers.
But federated learning has some drawbacks. Transferring a large machine-learning model to and from a central server involves moving a lot of data, which has high communication costs, especially since the model must be sent back and forth dozens or even hundreds of times. Plus, each user gathers their own data, so those data don’t necessarily follow the same statistical patterns, which hampers the performance of the combined model. And that combined model is made by taking an average — it is not personalized for each user.
The researchers developed a technique that can simultaneously address these three problems of federated learning. Their method boosts the accuracy of the combined machine-learning model while significantly reducing its size, which speeds up communication between users and the central server. It also ensures that each user receives a model that is more personalized for their environment, which improves performance.
The researchers were able to reduce the model size by nearly an order of magnitude when compared to other techniques, which led to communication costs that were between four and six times lower for individual users. Their technique was also able to increase the model’s overall accuracy by about 10 percent.
“A lot of papers have addressed one of the problems of federated learning, but the challenge was to put all of this together. Algorithms that focus just on personalization or communication efficiency don’t provide a good enough solution. We wanted to be sure we were able to optimize for everything, so this technique could actually be used in the real world,” says Vaikkunth Mugunthan PhD ’22, lead author of a paper that introduces this technique.
Mugunthan wrote the paper with his advisor, senior author Lalana Kagal, a principal research scientist in the Computer Science and Artificial Intelligence Laboratory (CSAIL). The work will be presented at the European Conference on Computer Vision.
Cutting a model down to size
The system the researchers developed, called FedLTN, relies on an idea in machine learning known as the lottery ticket hypothesis. This hypothesis says that within very large neural network models there exist much smaller subnetworks that can achieve the same performance. Finding one of these subnetworks is akin to finding a winning lottery ticket. (LTN stands for “lottery ticket network.”)
Neural networks, loosely based on the human brain, are machine-learning models that learn to solve problems using interconnected layers of nodes, or neurons.
Finding a winning lottery ticket network is more complicated than a simple scratch-off. The researchers must use a process called iterative pruning. If the model’s accuracy is above a set threshold, they remove nodes and the connections between them (just like pruning branches off a bush) and then test the leaner neural network to see if the accuracy remains above the threshold.
Other methods have used this pruning technique for federated learning to create smaller machine-learning models which could be transferred more efficiently. But while these methods may speed things up, model performance suffers.
Mugunthan and Kagal applied a few novel techniques to accelerate the pruning process while making the new, smaller models more accurate and personalized for each user.
They accelerated pruning by avoiding a step where the remaining parts of the pruned neural network are “rewound” to their original values. They also trained the model before pruning it, which makes it more accurate so it can be pruned at a faster rate, Mugunthan explains.
To make each model more personalized for the user’s environment, they were careful not to prune away layers in the network that capture important statistical information about that user’s specific data. In addition, when the models were all combined, they made use of information stored in the central server so it wasn’t starting from scratch for each round of communication.
They also developed a technique to reduce the number of communication rounds for users with resource-constrained devices, like a smart phone on a slow network. These users start the federated learning process with a leaner model that has already been optimized by a subset of other users.
Winning big with lottery ticket networks
When they put FedLTN to the test in simulations, it led to better performance and reduced communication costs across the board. In one experiment, a traditional federated learning approach produced a model that was 45 megabytes in size, while their technique generated a model with the same accuracy that was only 5 megabytes. In another test, a state-of-the-art technique required 12,000 megabytes of communication between users and the server to train one model, whereas FedLTN only required 4,500 megabytes.
With FedLTN, the worst-performing clients still saw a performance boost of more than 10 percent. And the overall model accuracy beat the state-of-the-art personalization algorithm by nearly 10 percent, Mugunthan adds.
Now that they have developed and finetuned FedLTN, Mugunthan is working to integrate the technique into a federated learning startup he recently founded, DynamoFL.
Moving forward, he hopes to continue enhancing this method. For instance, the researchers have demonstrated success using datasets that had labels, but a greater challenge would be applying the same techniques to unlabeled data, he says.
Mugunthan is hopeful this work inspires other researchers to rethink how they approach federated learning.
“This work shows the importance of thinking about these problems from a holistic aspect, and not just individual metrics that have to be improved. Sometimes, improving one metric can actually cause a downgrade in the other metrics. Instead, we should be focusing on how we can improve a bunch of things together, which is really important if it is to be deployed in the real world,” he says.
Science & Technology
Speedy robo-gripper reflexively organizes cluttered spaces
Rather than start from scratch after a failed attempt, the pick-and-place robot adapts in the moment to get a better hold

Written by Jennifer Chu, MIT News Office
Images/video: https://news.mit.edu/2023/speedy-robo-gripper-reflexively-organizes-spaces-0427
When manipulating an arcade claw, a player can plan all she wants. But once she presses the joystick button, it’s a game of wait-and-see. If the claw misses its target, she’ll have to start from scratch for another chance at a prize.
The slow and deliberate approach of the arcade claw is similar to state-of-the-art pick-and-place robots, which use high-level planners to process visual images and plan out a series of moves to grab for an object. If a gripper misses its mark, it’s back to the starting point, where the controller must map out a new plan.

Credits:Image: Jodi Hilton
Looking to give robots a more nimble, human-like touch, MIT engineers have now developed a gripper that grasps by reflex. Rather than start from scratch after a failed attempt, the team’s robot adapts in the moment to reflexively roll, palm, or pinch an object to get a better hold. It’s able to carry out these “last centimeter” adjustments (a riff on the “last mile” delivery problem) without engaging a higher-level planner, much like how a person might fumble in the dark for a bedside glass without much conscious thought.
The new design is the first to incorporate reflexes into a robotic planning architecture. For now, the system is a proof of concept and provides a general organizational structure for embedding reflexes into a robotic system. Going forward, the researchers plan to program more complex reflexes to enable nimble, adaptable machines that can work with and among humans in ever-changing settings.
“In environments where people live and work, there’s always going to be uncertainty,” says Andrew SaLoutos, a graduate student in MIT’s Department of Mechanical Engineering. “Someone could put something new on a desk or move something in the break room or add an extra dish to the sink. We’re hoping a robot with reflexes could adapt and work with this kind of uncertainty.”
SaLoutos and his colleagues will present a paper on their design in May at the IEEE International Conference on Robotics and Automation (ICRA). His MIT co-authors include postdoc Hongmin Kim, graduate student Elijah Stanger-Jones, Menglong Guo SM ’22, and professor of mechanical engineering Sangbae Kim, the director of the Biomimetic Robotics Laboratory at MIT.
High and low
Many modern robotic grippers are designed for relatively slow and precise tasks, such as repetitively fitting together the same parts on a a factory assembly line. These systems depend on visual data from onboard cameras; processing that data limits a robot’s reaction time, particularly if it needs to recover from a failed grasp.
“There’s no way to short-circuit out and say, oh shoot, I have to do something now and react quickly,” SaLoutos says. “Their only recourse is just to start again. And that takes a lot of time computationally.”
In their new work, Kim’s team built a more reflexive and reactive platform, using fast, responsive actuators that they originally developed for the group’s mini cheetah — a nimble, four-legged robot designed to run, leap, and quickly adapt its gait to various types of terrain.
The team’s design includes a high-speed arm and two lightweight, multijointed fingers. In addition to a camera mounted to the base of the arm, the team incorporated custom high-bandwidth sensors at the fingertips that instantly record the force and location of any contact as well as the proximity of the finger to surrounding objects more than 200 times per second.
The researchers designed the robotic system such that a high-level planner initially processes visual data of a scene, marking an object’s current location where the gripper should pick the object up, and the location where the robot should place it down. Then, the planner sets a path for the arm to reach out and grasp the object. At this point, the reflexive controller takes over.
If the gripper fails to grab hold of the object, rather than back out and start again as most grippers do, the team wrote an algorithm that instructs the robot to quickly act out any of three grasp maneuvers, which they call “reflexes,” in response to real-time measurements at the fingertips. The three reflexes kick in within the last centimeter of the robot approaching an object and enable the fingers to grab, pinch, or drag an object until it has a better hold.
They programmed the reflexes to be carried out without having to involve the high-level planner. Instead, the reflexes are organized at a lower decision-making level, so that they can respond as if by instinct, rather than having to carefully evaluate the situation to plan an optimal fix.
“It’s like how, instead of having the CEO micromanage and plan every single thing in your company, you build a trust system and delegate some tasks to lower-level divisions,” Kim says. “It may not be optimal, but it helps the company react much more quickly. In many cases, waiting for the optimal solution makes the situation much worse or irrecoverable.”
Cleaning via reflex
The team demonstrated the gripper’s reflexes by clearing a cluttered shelf. They set a variety of household objects on a shelf, including a bowl, a cup, a can, an apple, and a bag of coffee grounds. They showed that the robot was able to quickly adapt its grasp to each object’s particular shape and, in the case of the coffee grounds, squishiness. Out of 117 attempts, the gripper quickly and successfully picked and placed objects more than 90 percent of the time, without having to back out and start over after a failed grasp.
A second experiment showed how the robot could also react in the moment. When researchers shifted a cup’s position, the gripper, despite having no visual update of the new location, was able to readjust and essentially feel around until it sensed the cup in its grasp. Compared to a baseline grasping controller, the gripper’s reflexes increased the area of successful grasps by over 55 percent.
Now, the engineers are working to include more complex reflexes and grasp maneuvers in the system, with a view toward building a general pick-and-place robot capable of adapting to cluttered and constantly changing spaces.
“Picking up a cup from a clean table — that specific problem in robotics was solved 30 years ago,” Kim notes. “But a more general approach, like picking up toys in a toybox, or even a book from a library shelf, has not been solved. Now with reflexes, we think we can one day pick and place in every possible way, so that a robot could potentially clean up the house.”
This research was supported, in part, by Advanced Robotics Lab of LG Electronics and the Toyota Research Institute.
Science & Technology
Researchers 3D print a miniature vacuum pump
The device would be a key component of a portable mass spectrometer that could help monitor pollutants or perform medical diagnoses in remote parts of the world

Written by Adam Zewe, MIT News Office
Mass spectrometers are extremely precise chemical analyzers that have many applications, from evaluating the safety of drinking water to detecting toxins in a patient’s blood. But building an inexpensive, portable mass spectrometer that could be deployed in remote locations remains a challenge, partly due to the difficulty of miniaturizing the vacuum pump it needs to operate.
MIT researchers utilized additive manufacturing to take a major step toward solving this problem. They 3D printed a miniature version of a type of vacuum pump, known as a peristaltic pump, that is about the size of a human fist.
Their pump can create and maintain a vacuum that has an order of magnitude lower pressure than another type of commonly used pump. The unique design, which can be printed in one pass on a multimaterial 3D printer, prevents fluid or gas from leaking while minimizing heat from friction during the pumping process. This increases the lifetime of the device.

Credits:Image: Courtesy of the researchers
This pump could be incorporated into a portable mass spectrometer used to monitor soil contamination in isolated parts of the world, for instance. The device could also be ideal for use in geological survey equipment bound for Mars, since it would be cheaper to launch the lightweight pump into space.
“We are talking about very inexpensive hardware that is also very capable. With mass spectrometers, the 500-pound gorilla in the room has always been the issue of pumps. What we have shown here is groundbreaking, but it is only possible because it is 3D-printed. If we wanted to do this the standard way, we wouldn’t have been anywhere close,” says Luis Fernando Velásquez-García, a principal scientist in MIT’s Microsystems Technology Laboratories (MTL) and senior author of a paper describing the new pump.
Velásquez-García is joined on the paper by lead author Han-Joo Lee, a former MIT postdoc; and Jorge Cañada Pérez-Sala, an electrical engineering and computer science graduate student. The paper appears today in Additive Manufacturing.
Pump problems
As a sample is pumped through a mass spectrometer, it is hit with an electric charge to turn its atoms into ions. An electromagnetic field manipulates these ions in a vacuum so their masses can be determined. This information can be used to identify the molecules in the sample. Maintaining the vacuum is key because if the ions collide with gas molecules from the air, their dynamics will change.
Peristaltic pumps are commonly used to move fluids or gases that would contaminate the pump’s components, such as reactive chemicals. The substance is entirely contained within a flexible tube that is looped around a set of rollers. The rollers squeeze the tube against its housing as they rotate. The pinched parts of the tube expand in the wake of the rollers, creating a vacuum that draws the liquid or gas through the tube.
While the pumps do create a vacuum, design problems have limited their use in mass spectrometers. The tube material redistributes when force is applied by the rollers, leading to gaps that cause leaks. This problem can be overcome by operating the pump rapidly, forcing the fluid through faster than it can leak out. But this causes excessive heat that damages the pump, and the gaps remain. To fully seal the tube and create the vacuum needed for a mass spectrometer, the mechanism must exert additional force to squeeze the bulged areas, causing more damage, explains Velásquez-García.
An additive solution
He and his team rethought the peristaltic pump design from the bottom up, looking for ways they could use additive manufacturing to make improvements. First, by using a multimaterial 3D printer, they were able to make the flexible tube out of a special type of hyperelastic material that can withstand a huge amount of deformation.
Then, through an iterative design process, they determined that adding notches to the walls of the tube would reduce the stress on the material when squeezed. With notches, the tube material does not need to redistribute to counteract the force from the rollers.
The precision afforded by 3D printing enabled the researchers to produce the exact notch size needed to eliminate the gaps. They were also able to vary the tube’s thickness so the walls are stronger in areas where connectors attach, further reducing stress on the material.
Using a multimaterial 3D printer, they printed the entire tube in one pass, which is important since postassembly can introduce defects that can cause leaks. To do this, they had to find a way to print the narrow, flexible tube vertically while preventing it from wobbling during the process. In the end, they created a lightweight structure that stabilizes the tube during printing but can be easily peeled off later without damaging the device.
“One of the key advantages of using 3D printing is that it allows us to aggressively prototype. If you do this work in a clean room, where a lot of these miniaturized pumps are made, it takes a lot of time. If you want to make a change, you have to start the entire process over. In this case, we can print our pump in a matter of hours, and every time it can be a new design,” Velásquez-García says.
Portable, yet performant
When they tested their final design, the researchers found that it was able to create a vacuum that had an order of magnitude lower pressure than state-of-the-art diaphragm pumps. Lower pressure yields a higher-quality vacuum. To reach that same pressure with standard pumps, one would need to connect three in a series, Velásquez-García says.
The pump reached a maximum temperature of 50 degrees Celsius, half that of state-of-the-art pumps used in other studies, and only required half as much force to fully seal the tube.
In the future, the researchers plan to explore ways to further reduce the maximum temperature, which would enable the tube to actuate faster, creating a better vacuum and increasing the flow rate. They are also working to 3D print an entire miniaturized mass spectrometer. As they develop that device, they will continue fine-tuning the specifications of the peristaltic pump.
“Some people think that when you 3D print something there must be some kind of tradeoff. But here our group has shown that is not the case. It really is a new paradigm. Additive manufacturing is not going to solve all the problems of the world, but it is a solution that has real legs,” Velásquez-García says.
This work was supported, in part, by the Empiriko Corporation.
Science & Technology
Miniscule device could help preserve the battery life of tiny sensors
Researchers demonstrate a low-power “wake-up” receiver one-tenth the size of other devices

Written by Adam Zewe, MIT News Office
Scientists are striving to develop ever-smaller internet-of-things devices, like sensors tinier than a fingertip that could make nearly any object trackable. These diminutive sensors have miniscule batteries which are often nearly impossible to replace, so engineers incorporate wake-up receivers that keep devices in low-power “sleep” mode when not in use, preserving battery life.
Researchers at MIT have developed a new wake-up receiver that is less than one-tenth the size of previous devices and consumes only a few microwatts of power. Their receiver also incorporates a low-power, built-in authentication system, which protects the device from a certain type of attack that could quickly drain its battery.
Many common types of wake-up receivers are built on the centimeter scale since their antennas must be proportional to the size of the radio waves they use to communicate. Instead, the MIT team built a receiver that utilizes terahertz waves, which are about one-tenth the length of radio waves. Their chip is barely more than 1 square millimeter in size.
They used their wake-up receiver to demonstrate effective, wireless communication with a signal source that was several meters away, showcasing a range that would enable their chip to be used in miniaturized sensors.
For instance, the wake-up receiver could be incorporated into microrobots that monitor environmental changes in areas that are either too small or hazardous for other robots to reach. Also, since the device uses terahertz waves, it could be utilized in emerging applications, such as field-deployable radio networks that work as swarms to collect localized data.
“By using terahertz frequencies, we can make an antenna that is only a few hundred micrometers on each side, which is a very small size. This means we can integrate these antennas to the chip, creating a fully integrated solution. Ultimately, this enabled us to build a very small wake-up receiver that could be attached to tiny sensors or radios,” says Eunseok Lee, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on the wake-up receiver.
Lee wrote the paper with his co-advisors and senior authors Anantha Chandrakasan, dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science, who leads the Energy-Efficient Circuits and Systems Group, and Ruonan Han, an associate professor in EECS, who leads the Terahertz Integrated Electronics Group in the Research Laboratory of Electronics; as well as others at MIT, the Indian Institute of Science, and Boston University. The research is being presented at the IEEE Custom Integrated Circuits Conference.
Scaling down the receiver
Terahertz waves, found on the electromagnetic spectrum between microwaves and infrared light, have very high frequencies and travel much faster than radio waves. Sometimes called “pencil beams,” terahertz waves travel in a more direct path than other signals, which makes them more secure, Lee explains.
However, the waves have such high frequencies that terahertz receivers often multiply the terahertz signal by another signal to alter the frequency, a process known as frequency mixing modulation. Terahertz mixing consumes a great deal of power.
Instead, Lee and his collaborators developed a zero-power-consumption detector that can detect terahertz waves without the need for frequency mixing. The detector uses a pair of tiny transistors as antennas, which consume very little power.
Even with both antennas on the chip, their wake-up receiver was only 1.54 square millimeters in size and consumed less than 3 microwatts of power. This dual-antenna setup maximizes performance and makes it easier to read signals.
Once received, their chip amplifies a terahertz signal and then converts analog data into a digital signal for processing. This digital signal carries a token, which is a string of bits (0s and 1s). If the token corresponds to the wake-up receiver’s token, it will activate the device.
Ramping up security
In most wake-up receivers, the same token is reused multiple times, so an eavesdropping attacker could figure out what it is. Then the hacker could send a signal that would activate the device over and over again, using what is called a denial-of-sleep attack.
“With a wake-up receiver, the lifetime of a device could be improved from one day to one month, for instance, but an attacker could use a denial-of-sleep attack to drain that entire battery life in even less than a day. That is why we put authentication into our wake-up receiver,” he explains.
They added an authentication block that utilizes an algorithm to randomize the device’s token each time, using a key that is shared with trusted senders. This key acts like a password — if a sender knows the password, they can send a signal with the right token. The researchers do this using a technique known as lightweight cryptography, which ensures the entire authentication process only consumes a few extra nanowatts of power.
They tested their device by sending terahertz signals to the wake-up receiver as they increased the distance between the chip and the terahertz source. In this way, they tested the sensitivity of their receiver — the minimum signal power needed for the device to successfully detect a signal. Signals that travel farther have less power.
“We achieved 5- to 10-meter longer distance demonstrations than others, using a device with a very small size and microwatt level power consumption,” Lee says.
But to be most effective, terahertz waves need to hit the detector dead-on. If the chip is at an angle, some of the signal will be lost. So, the researchers paired their device with a terahertz beam-steerable array, recently developed by the Han group, to precisely direct the terahertz waves. Using this technique, communication could be sent to multiple chips with minimal signal loss.
In the future, Lee and his collaborators want to tackle this problem of signal degradation. If they can find a way to maintain signal strength when receiver chips move or tilt slightly, they could increase the performance of these devices. They also want to demonstrate their wake-up receiver in very small sensors and fine-tune the technology for use in real-world devices.
“We have developed a rich technology portfolio for future millimeter-sized sensing, tagging, and authentication platforms, including terahertz backscattering, energy harvesting, and electrical beam steering and focusing. Now, this portfolio is more complete with Eunseok’s first-ever terahertz wake-up receiver, which is critical to save the extremely limited energy available on those mini platforms,” Han says.
Additional co-authors include Muhammad Ibrahim Wasiq Khan PhD ’22; Xibi Chen, an EECS graduate student; Ustav Banerjee PhD ’21, an assistant professor at the Indian Institute of Science; Nathan Monroe PhD ’22; and Rabia Tugce Yazicigil, an assistant professor of electrical and computer engineering at Boston University.
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