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Even in decentralized learning, we show that data can be leaked through the training of machine learning models, motivating the need for protection mechanisms. While differential privacy is the gold standard for centralized privacy-preserving machine learning, it is not well suited to decentralized learning, where participants collaboratively train a model via peer-to-peer messages. We present a relaxation of differential privacy that captures a relevant trust setting for decentralized learning. We show that decentralization can lead to a better privacy-utility tradeoff in this regime.

We start with a brief overview of the current approach of federated learning (FL) and differential privacy (DP) for private training of small LMs, empowering production on-device decoding LMs in mobile keyboard applications. Recent technical advances including new BLT-DP-FTRL algorithm offers strong privacy-utility trade-offs and ease-of-use in deployment, the SI-CIFG model architecture for efficient on-device training and compatibility with DP, and synthetic data from LLMs to improve (public) pre-training. The dedication to privacy-preserving learning to improve small LMs has not only delivered substantial user benefits, but has also helped improve LLMs in mobile typing applications, bridged by synthetic data. Next, we share our exploration over the past few years on generating and using synthetic data to improve LMs. We focus on approaches adhering to the privacy principles of both data minimization and data anonymization, and show how they are making a real-world impact in small and large models. We discuss synthetic data usage in complicated training paradigms and algorithms to improve quality and efficiency of synthetic data generation. Finally, we discuss implications of recent trends in federated learning, open problems and a preliminary study on personalization.

Differentially private training methods typically rely on injecting external noise at each iteration, as in DP-SGD, to limit the influence of individual data points. In this talk, we will explore how inherent algorithmic randomness already embedded in modern AI training pipelines for non-privacy reasons can be harnessed for privacy amplification, thereby reducing reliance on externally injected noise.

Prior work has studied privacy amplification through user or data subsampling, but largely under idealized assumptions such as independent Poisson subsampling. In practice, training pipelines exhibit more structured, system-driven forms of randomness. The goal of this talk is twofold: first, to move beyond idealized subsampling models toward structured sampling mechanisms that better reflect real-world constraints; and second, to investigate additional sources of algorithmic randomness, including model partitioning, dropout, and compression, that naturally limit how much information any single sample or user contributes to the final model. We will discuss how these mechanisms can be rigorously quantified to strengthen privacy guarantees at scale.

Decoupled training methods, such as DiLoCo, relax the requirement for frequent synchronization, allowing large-scale models to be trained across distributed, high-latency compute clusters. This talk examines recent progress in making these methods work at scale and the ongoing development of "optimization for decoupled training." We then explore how the empirical success of decoupled training has unexpectedly influenced "standard" optimization, leading to new lookahead-inspired Nesterov-style methods. Finally, we highlight open questions at the intersection of decoupled training and federated learning. Throughout, we discuss key similarities and distinctions between these fields and ruminate on how heterogeneity, typically seen as a hurdle in federated settings, might actually serve as a powerful tool for improving decoupled training when injected intentionally.

Data heterogeneity is a pervasive challenge in modern machine learning, yet it is typically studied in isolation within separate subfields. For instance, in federated learning, statistical heterogeneity across clients causes local models to drift and overspecialize; in continual learning, temporal heterogeneity in data distributions leads to catastrophic forgetting; in model merging and modular approaches, independently trained components make their composition fragile and unpredictable. In this talk, I argue that these are manifestations of the same fundamental tension: learning from diverse, non-stationary, and decentralized sources, while preserving and composing acquired knowledge. I will present a unified perspective that connects these three settings and show how insights can transfer across them, pointing toward a common research agenda: developing methods that embrace heterogeneity as a design principle rather than treating it as an obstacle to overcome.

Modern machine learning often requires training with large batch sizes, distributed data, and massively parallel compute hardware. While communication becomes a major bottleneck in such settings, methods like Local Stochastic Gradient Descent (Local SGD) show great promise in reducing this overhead. Local SGD consists of three distinct parts: a local optimization process, an aggregation mechanism, and an outer optimizer that uses aggregated updates to produce a new model.
While extensive literature exists on understanding hyperparameters in the local optimization process, the choice of the outer optimizer and its hyperparameters is less clear. In this talk, we will explore the role of the outer optimizer in Local SGD and present new convergence guarantees for the algorithm. In particular, we will demonstrate that tuning the outer learning rate allows us to (a) trade off between optimization error and stochastic gradient noise variance, and (b) compensate for the ill-tuning of the inner learning rate. Our theory suggests that the outer learning rate should sometimes be set to values greater than 1.
Furthermore, we will extend these results to settings using momentum in the outer optimizer, showing a similar role for the momentum-adjusted outer learning rate. We will also discuss acceleration in the outer optimizer, showing that it improves the convergence rate as a function of the number of communication rounds, improving upon prior algorithms that apply acceleration locally. Finally, we will discuss some experimental validation for our theoretical results and some avenues for future work.

Reinforcement learning (RL), concerning decision making in uncertain environments, lies at the heart of modern artificial intelligence. Due to the high dimensionality, training of RL agents typically requires a significant amount of computation and data to achieve desirable performance. However, data collection can be extremely time-consuming with limited access in real-world applications, especially when performed by a single agent. On the other hand, it is plausible to leverage multiple agents to collect data simultaneously, under the premise that they can learn a global policy collaboratively without the need of sharing local data in a federated manner. This talk addresses the fundamental statistical and communication trade-offs in the algorithmic designs of federated RL algorithms, covering both blessings and curses in the presence of data and task heterogeneities across the agents.

In federated or decentralized environments, tasks clients solve can exhibit different gradients (along with gradient statistics), or even completely different problem semantics (e.g., solving a biology problem versus a CS problem). These forms of heterogeneity pose significant challenges to training and inference. For instance, to realize the benefits of adaptive optimization, it is critical to address heterogeneous preconditioners accumulated on the client side. I first introduce an optimizer Bi^2Clip that approximates adaptive methods without maintaining preconditioners. It leverages coordinate-wise bi-directional clipping, which also helps mitigate issues such as heavy-tailed gradient noise. I then discuss an asynchronous framework based on it that can tolerate heterogeneous hardware with improved dependence on the staleness. Lastly, we shift from gradient/parameter space to semantic space, and present a federation-over-text (FoT) framework designed for heterogeneous tasks and domains. In FoT, instead of transmitting gradients or models, clients iteratively share metacognitive summaries of their local reasoning and planning processes to build a library of reusable insights. I discuss its early applications on math problems solving and ML research insight discovery. Overall, these results show that carefully treating heterogeneity can result in meaningful gains in both final performance and learning/inference efficiency.