Few-shot fine-tuning of Diffusion Models (DMs) is a key advancement, significantly reducing training costs and enabling personalized AI applications. However, we explore the training dynamics of DMs and observe an unanticipated phenomenon: during the training process, image fidelity initially improves, then unexpectedly deteriorates with the emergence of noisy patterns, only to recover later with severe overfitting. We term the stage with generated noisy patterns as corruption stage. To understand this corruption stage, we begin by heuristically modeling the one-shot fine-tuning scenario, and then extend this modeling to more general cases. Through this modeling, we identify the primary cause of this corruption stage: a narrowed learning distribution inherent in the nature of few-shot fine-tuning. To tackle this, we apply Bayesian Neural Networks (BNNs) on DMs with variational inference to implicitly broaden the learned distribution, and present that the learning target of the BNNs can be naturally regarded as an expectation of the diffusion loss and a further regularization with the pretrained DMs. This approach is highly compatible with current few-shot fine-tuning methods in DMs and does not introduce any extra inference costs. Experimental results demonstrate that our method significantly mitigates corruption, and improves the fidelity, quality and diversity of the generated images in both object-driven and subject-driven generation tasks.

Federated Learning (FL) is increasingly vulnerable to model poisoning attacks, where malicious clients degrade the global model's accuracy with manipulated updates. Unfortunately, most existing defenses struggle to handle the scenarios when multiple adversaries exist, and often rely on historical or validation data, rendering them ill-suited for the dynamic and diverse nature of real-world FL environments. Exacerbating these limitations is the fact that most existing defenses also fail to account for the distinctive contributions of Deep Neural Network (DNN) layers in detecting malicious activity, leading to the unnecessary rejection of benign updates. To bridge these gaps, we introduce FedRoLa, a cutting-edge similarity-based defense method optimized for FL. Specifically, FedRoLa leverages global model parameters and client updates independently, moving away from reliance on historical or validation data. It features a unique layer-based aggregation with dynamic layer selection, enhancing threat detection, and includes a dynamic probability method for balanced security and model performance. Through comprehensive evaluations using different DNN models and real-world datasets, FedRoLa demonstrates substantial improvements over the status quo approaches in global model accuracy, achieving up to 4% enhancement in terms of accuracy, reducing false positives to 6.4%, and securing an 92.8% true positive rate.

Deep reinforcement learning (DRL) has demonstrated significant potential in various applications, including gaming AI, robotics, and system scheduling. DRL algorithms produce, sample, and learn from training data online through a trial-and-error process, demanding considerable time and computational resources. To address this, distributed DRL algorithms and paradigms have been developed to expedite training using extensive resources. Through carefully designed experiments, we are the first to observe that strategically increasing the actor-environment interactions by spawning more concurrent actors at certain training rounds within ephemeral time frames can significantly enhance training efficiency. Yet, current distributed DRL solutions, which are predominantly server-based (or serverful), fail to capitalize on these opportunities due to their long startup times, limited adaptability, and cumbersome scalability. This paper proposes Nitro , a generic training engine for distributed DRL algorithms that enforces timely and effective boosting with concurrent actors instantaneously spawned by serverless computing. With serverless functions, Nitro adjusts data sampling strategies dynamically according to the DRL training demands. Nitro seizes the opportunity of real-time boosting by accurately and swiftly detecting an empirical metric. To achieve cost efficiency, we design a heuristic actor scaling algorithm to guide Nitro for cost-aware boosting budget allocation. We integrate Nitro with state-of-the-art DRL algorithms and frameworks and evaluate them on AWS EC2 and Lambda. Experiments with Mujoco and Atari benchmarks show that Nitro improves the final rewards ( i.e. , training quality) by up to 6× and reduces training costs by up to 42%.

Federated learning (FL) is a distributed optimization paradigm that learns from data samples distributed across a number of clients. Adaptive client selection that is cognizant of the training progress of clients has become a major trend to improve FL efficiency but not yet well-understood. Most existing FL methods such as FedAvg and its state-of-the-art variants implicitly assume that all learning phases during the FL training process are equally important. Unfortunately, this assumption has been revealed to be invalid due to recent findings on critical learning periods (CLP), in which small gradient errors may lead to an irrecoverable deficiency on final test accuracy. In this paper, we develop CriticalFL, a CLP augmented FL framework to reveal that adaptively augmenting exiting FL methods with CLP, the resultant performance is significantly improved when the client selection is guided by the discovered CLP. Experiments based on various machine learning models and datasets validate that the proposed CriticalFL framework consistently achieves an improved model accuracy while maintains better communication efficiency as compared to state-of-the-art methods, demonstrating a promising and easily adopted method for tackling the heterogeneity of FL training.

Recently, personalized federated learning (pFL) has attracted increasing attention in privacy protection, collaborative learning, and tackling statistical heterogeneity among clients, e.g., hospitals, mobile smartphones, etc. Most existing pFL methods focus on exploiting the global information and personalized information in the client-level model parameters while neglecting that data is the source of these two kinds of information. To address this, we propose the Federated Conditional Policy (FedCP) method, which generates a conditional policy for each sample to separate the global information and personalized information in its features and then processes them by a global head and a personalized head, respectively. FedCP is more fine-grained to consider personalization in a sample-specific manner than existing pFL methods. Extensive experiments in computer vision and natural language processing domains show that FedCP outperforms eleven state-of-the-art methods by up to 6.69%. Furthermore, FedCP maintains its superiority when some clients accidentally drop out, which frequently happens in mobile settings. Our code is public at https://github.com/TsingZ0/FedCP.

Serverless computing automates fine-grained resource scaling and simplifies the development and deployment of online services with stateless functions. However, it is still non-trivial for users to allocate appropriate resources due to various function types, dependencies, and input sizes. Misconfiguration of resource allocations leaves functions either under-provisioned or over-provisioned and leads to continuous low resource utilization. This paper presents Freyr, a new resource manager (RM) for serverless platforms that maximizes resource efficiency by dynamically harvesting idle resources from over-provisioned functions to under-provisioned functions. Freyr monitors each function’s resource utilization in real-time, detects over-provisioning and under-provisioning, and learns to harvest idle resources safely and accelerates functions efficiently by applying deep reinforcement learning algorithms along with a safeguard mechanism. We have implemented and deployed a Freyr prototype in a 13-node Apache OpenWhisk cluster. Experimental results show that 38.8% of function invocations have idle resources harvested by Freyr, and 39.2% of invocations are accelerated by the harvested resources. Freyr reduces the 99th-percentile function response latency by 32.1% compared to the baseline RMs.