Google is reportedly working on a new Android API for what it’s calling Rich Ongoing Notifications, which would allow apps to display at-a-glance information in a status bar much like Apple’s Live Activities in the Dynamic Island on iPhone. This is according to journalist , who spotted the code in the Android 15 QPR1 Beta 3 release. It could work a lot like the time tracker that currently appears when you’re on a phone call, with a bit of text in a bubble at the top of the display that you can tap to open the app for more details.

Writing for Android Authority, Rahman says the API “will let apps create chips with their own text and background color that live in the status bar.” It could be especially useful for things like transit updates, allowing users to keep track of pertinent information like departure times or an Uber’s ETA while using other apps. The feature isn’t yet complete, though, and it could still be some time before we see it. Rahman predicts it’ll arrive with Android 16.

Each year, cyberattacks become more frequent and data breaches become more expensive. Whether companies seek to protect their AI system during development or use their algorithm to improve their security posture, they must alleviate cybersecurity risks. Federated learning might be able to do both.

What is federated learning?

Federated learning is an approach to AI development in which multiple parties train a single model separately. Each downloads the current primary algorithm from a central cloud server. They train their configuration independently on local servers, uploading it upon completion. This way, they can share data remotely without exposing raw data or model parameters.

The centralized algorithm weighs the number of samples it receives from each disparately trained configuration, aggregating them to create a single global model. All information remains on each participant’s local servers or devices — the centralized repository weighs the updates instead of processing raw data.

Federated learning’s popularity is rapidly increasing because it addresses common development-related security concerns. It is also highly sought after for its performance advantages. Research shows this technique can improve an image classification model’s accuracy by up to 20% — a substantial increase.

Horizontal federated learning

There are two types of federated learning. The conventional option is horizontal federated learning. In this approach, data is partitioned across various devices. The datasets share feature spaces but have different samples. This enables edge nodes to collaboratively train a machine learning (ML) model without sharing information.

Vertical federated learning

In vertical federated learning, the opposite is true — features differ, but samples are the same. Features are distributed vertically across participants, each possessing different attributes about the same set of entities. Since just one party has access to the complete set of sample labels, this approach preserves privacy. 

How federated learning strengthens cybersecurity

Traditional development is prone to security gaps. Although algorithms must have expansive, relevant datasets to maintain accuracy, involving multiple departments or vendors creates openings for threat actors. They can exploit the lack of visibility and broad attack surface to inject bias, conduct prompt engineering or exfiltrate sensitive training data.

When algorithms are deployed in cybersecurity roles, their performance can affect an organization’s security posture. Research shows that model accuracy can suddenly diminish when processing new data. Although AI systems may appear accurate, they may fail when tested elsewhere because they learned to take bogus shortcuts to produce convincing results.

Since AI cannot think critically or genuinely consider context, its accuracy diminishes over time. Even though ML models evolve as they absorb new information, their performance will stagnate if their decision-making skills are based on shortcuts. This is where federated learning comes in.

Other notable benefits of training a centralized model via disparate updates include privacy and security. Since every participant works independently, no one has to share proprietary or sensitive information to progress training. Moreover, the fewer data transfers there are, the lower the risk of a man-in-the-middle attack (MITM).

All updates are encrypted for secure aggregation. Multi-party computation hides them behind various encryption schemes, lowering the chances of a breach or MITM attack. Doing so enhances collaboration while minimizing risk, ultimately improving security posture.

One overlooked advantage of federated learning is speed. It has a much lower latency than its centralized counterpart. Since training happens locally instead of on a central server, the algorithm can detect, classify and respond to threats much faster. Minimal delays and rapid data transmissions enable cybersecurity professionals to handle bad actors with ease.

Considerations for cybersecurity professionals

Before leveraging this training technique, AI engineers and cybersecurity teams should consider several technical, security and operational factors.

Resource usage

AI development is expensive. Teams building their own model should expect to spend anywhere from $5 million to $200 million upfront, and upwards of $5 million annually for upkeep. The financial commitment is significant even with costs spread out among multiple parties. Business leaders should account for cloud and edge computing costs.

Federated learning is also computationally intensive, which may introduce bandwidth, storage space or computing limitations. While the cloud enables on-demand scalability, cybersecurity teams risk vendor lock-in if they are not careful. Strategic hardware and vendor selection is of the utmost importance.

Participant trust

While disparate training is secure, it lacks transparency, making intentional bias and malicious injection a concern. A consensus mechanism is essential for approving model updates before the centralized algorithm aggregates them. This way, they can minimize threat risk without sacrificing confidentiality or exposing sensitive information.

Training data security

While this machine learning training technique can improve a firm’s security posture, there is no such thing as 100% secure. Developing a model in the cloud comes with the risk of insider threats, human error and data loss. Redundancy is key. Teams should create backups to prevent disruption and roll back updates, if necessary. 

Decision-makers should revisit their training datasets’ sources. In ML communities, heavy borrowing of datasets occurs, raising well-founded concerns about model misalignment. On Papers With Code, more than 50% of task communities use borrowed datasets at least 57.8% of the time. Moreover, 50% of the datasets there come from just 12 universities.

Applications of federated learning in cybersecurity

Once the primary algorithm aggregates and weighs participants’ updates, it can be reshared for whatever application it was trained for. Cybersecurity teams can use it for threat detection. The advantage here is twofold — while threat actors are left guessing since they cannot easily exfiltrate data, professionals pool insights for highly accurate output.

Federated learning is ideal for adjacent applications like threat classification or indicator of compromise detection. The AI’s large dataset size and extensive training build its knowledge base, curating expansive expertise. Cybersecurity professionals can use the model as a unified defense mechanism to protect broad attack surfaces.

ML models — especially those that make predictions — are prone to drift over time as concepts evolve or variables become less relevant. With federated learning, teams could periodically update their model with varied features or data samples, resulting in more accurate, timely insights.

Leveraging federated learning for cybersecurity

Whether companies want to secure their training dataset or leverage AI for threat detection, they should consider using federated learning. This technique could improve accuracy and performance and strengthen their security posture as long as they strategically navigate potential insider threats or breach risks.

 Zac Amos is the features editor at ReHack.

The co-founders who sold their last startup to Spotify are working on a new project: an AI-powered educational startup called Oboe backed by a $4 million seed investment. The new company, hailing from Nir Zicherman and Michael Mignano, aims to democratize access to learning the way that their prior startup, Anchor, made it possible for anyone to create a podcast. That is, Oboe intends to produce a user-friendly interface that helps people accomplish the task at hand — in this case, expanding their knowledge via a combination of AI technology, audio, and video.

“This idea is something that Mike and I have been talking about for a long time now, because we have both felt for a while that there is a really big opportunity in the education space — much bigger than I think a lot of people realize,” Zicherman says.

After taking a brief period to recharge after leaving Spotify in October 2023, Zicherman was soon ready to roll up his sleeves and build something new with a small team, he says, similar to Anchor’s early days. He also took inspiration from his work at Spotify, where he had spent the last few years building out its audiobooks business and scaling it to more markets.

“One of the big things … that drew me to audiobooks, as a business, and as a product, was this idea of enabling so many more people than ever before to get access to incredible, high-quality content, including educational content and making that really ubiquitous,” he notes.

Oboe looks to expand on that mission, but not via audiobooks.

Instead, the team envisions a product that would allow more people to engage with “active learning journeys,” as the company calls it, by offering learning tools that optimize the development of a curriculum and personalize those to the way the individual user learns most effectively.

The tools offered will be available across platforms and will involve native applications, similar to existing online learning services.

However, the startup intends to differentiate itself from others in the space by leveraging AI to both customize the curriculum materials and enable an interactive experience. For instance, synthetic AI voices will be a part of the offering. Meanwhile, machine learning combined with Oboe’s back-end architecture will help to personalize how the material is presented and will improve over time.

Because AI tends to hallucinate or cite bad information, part of Oboe’s secret sauce will be focused on ensuring the content is accurate, high-quality, and scalable.

In part, Oboe will rely on third-party foundational AI models, but the team is also undertaking a “significant” amount of work in-house to build its data architecture and optimize the curriculum on a per-user basis, Zicherman says.

“This product is not one of these thin wrappers around existing LLMs. There’s a lot more happening under the hood,” he teases.

In addition, access to the material will be made available across different formats. When you can’t look at a screen — like if out for a jog or driving to work — you could tune in via audio. Other times, you may be watching videos, using an app, or engaging with a website.

Initially, Oboe will target just a few verticals, ranging from someone teaching themselves programming to a college student supplementing their classroom experience with more materials, for instance. These debut courses will focus on learners older than the K through 12 demographic, but Oboe’s eventual goal is to fulfill its mission of “making humanity smarter.” (A tall order, indeed.) That includes the K through 12 and higher-ed space, as well as those upskilling for their careers, or just engaged on their own to learn something new, like playing a new instrument. (Fun fact: Not only is the oboe the instrument an orchestra tunes to, but it’s also the root of the Japanese word meaning “to learn.”)

New York-based Oboe isn’t yet ready to share much more in terms of product details, but it has raised funding from a crowd of investors, including those who have worked with Zicherman and Mignano previously. Mignano will remain a full-time partner at Lightspeed but will serve on the board of this new company and support Zicherman in his role of CEO, he says.

“In my co-founding role at Oboe, Nir and I have worked closely together to set the company up for success through its initial strategy and product direction,” Mignano tells TechCrunch. “My partners at Lightspeed are super supportive of me being both investor and founder — there’s a long history of our investors starting or incubating their own companies. Nir and I were thrilled to raise this initial round from a number of amazing seed funds and angels — many who backed us previously at Anchor,” he adds.

Oboe’s $4 million seed round was led by Eniac Ventures — the VC firm that led Anchor’s seed. The round also includes investment from Haystack, Factorial Capital, Homebrew, Offline Ventures, Scott Belsky, Kayvon Beykpour, Nikita Bier, Tim Ferriss, and Matt Lieber.

Android 16 may come with a new “Rich Ongoing Notifications” feature that lets developers keep persistent notifications in the Android status bar, according to Android 15 beta code discovered by Android Authority’s Mishaal Rahman, who frequently dives into code to surface coming features.

Right now, the code enables adding a pill-shaped icon with custom text and background color to the Android status bar. Some mock notifications Rahman created show how it could be used for things like telling you when to expect your Uber to arrive. Android has already had a feature like this since Android 12 that lets you know how long you’ve been on a phone call, Rahman notes.

It looks similar to the iOS Live Activities feature, which surfaces things like timers, sports scores, and delivery ETAs on users’ lock screens and at the top of notifications. On the iPhone 14 Pro and up, they appear as widgets that are a glance away in the Dynamic Island cutout while you’re doing other things on your phone.

I’d love to have something just like that on my Pixel 6 phone. Live Activities have been a great way to keep from forgetting about a parking meter I paid for via my city’s parking app, or quickly checking on when a food delivery will arrive. Having something similar on Android would be one less barrier to me switching back to the platform in the future.

After Apple’s reveal of the M4 chip earlier this year, and its significant performance improvements (which we discuss at length in our M4 iPad Pro review), all eyes are now on what the company has in store for its Mac and MacBook devices, which at the moment are still using M3 chips.

We’ve just had the biggest hint yet that M4-powered Macs are about to drop thanks to Apple’s VP of Worldwide Marketing, Greg Joswiak, who teased on X that an upcoming ‘week of announcements’ is set to kick off on Monday, October 28. Along with recent rumors surrounding production of the M4 MacBook Air, it looks like it could be a very exciting week for Apple fans – so here’s what we expect to see.

M4 MacBook Pro

With Apple’s M4 Pro and Max chips yet to be unveiled, this upcoming showcase could be the ideal opportunity to show off the powerful M4 variants, especially as the M4 MacBook Pro 14-inch and 16-inch models will almost certainly be revealed, with Apple hopefully highlighting the major performance leap from the previous M3 Pro and Max chips.

Indian Space Research Organisation (ISRO) on Friday announced that Group Captain Shubhanshu Shukla is chosen to fly on the upcoming Indo-US mission to the International Space Station (ISS). Shukla is designated as the prime astronaut to fly on the mission, while Group Captain Prasanth Balakrishnan Nair will be the backup astronaut. 

In an official release, the ISRO said its Human Space Flight Centre has entered into a space flight agreement with US’ Axiom Space Inc. For the mission, the National Mission Assignment Board has recommended two ‘gaganyatris’. 

The Board has “recommended two ‘gaganyatris’ (space travellers) –Group Captain Shukla (prime) and Group Captain Nair (backup),” read the statement. 

“The assigned crew members will be finally approved to fly to the International Space Station by the Multilateral Crew Operations Panel (MCOP). The recommended gaganyatris will commence their training for the mission from the first week of August 2024,” said ISRO.

Group Captain Prasanth Balakrishnan Nair | X

The ‘gaganyatris’ will be undertaking selected scientific research and technology demonstration experiments on board the ISS and engage in space outreach activities, it added.

“The experiences gained during this mission will be beneficial for the Indian Human Space Programme and it will also strengthen human space flight cooperation between ISRO and NASA,” the Indian space agency said.

A joint statement issued by ISRO and NASA IN June last year had envisioned a joint ISRO-NASA mission to the space station. The decision was announced after Prime Minister Narendra Modi’s official state visit to the US. 

Who is Group Shubhanshu Shukla? 

Born in Uttar Pradesh’s Lucknow in 1985, Shukla is an alumnus of the National Defence Academy. He was commissioned on June 17, 2006 in the fighter stream of the Indian Air Force (IAF). 

He is a Fighter Combat Leader and a test pilot with approximately 2,000 hours of flying experience. He has flown a variety of aircraft including Sukhoi-30MKI, MiG-21, MiG-29, Jaguar, Hawk, Dornier, and An-32, among others.

Who is Group Captain Prasanth Balakrishnan Nair? 

Born in Kerala’s Thiruvazhiyad on August 26, 1976, he is also an alumnus of the National Defence Academy and a recipient of the Sword of Honour at the Air Force Academy. He was commissioned on December 19, 1998 in the fighter stream of the IAF. 

Group Captain Nair is a Category-A flying Instructor – the highest that a pilot can achieve, and a test pilot with approximately 3,000 hours of flying experience. He has also flown several aircraft including Sukhoi-30MKI, MiG-21, MiG-29, Hawk, Dornier and An-32.

He is an alumnus of the United States Staff College and a Directing Staff at the Defence Services Staff College, Wellington and the Flying Instructors School, Tambaram.