Sharing is Learning: A New Approach to Collaborative AI

Author: Denis Avetisyan

A novel framework leverages knowledge markets to dramatically reduce communication costs in federated learning, enabling more efficient and effective AI collaboration.

A federated learning study demonstrates that KTA v2 achieves 57.7% accuracy on the CIFAR-10 dataset using only approximately 3.8 MB of communication, significantly outperforming FedAvg, which requires roughly 4265.5 MB to reach 42.1% accuracy on the same task.

This paper introduces KTA v2, a prediction-space knowledge market for communication-efficient federated learning on non-IID multimedia tasks and large models.

Despite the promise of collaborative learning, federated learning systems often struggle with communication bottlenecks and performance degradation under realistic data heterogeneity. This paper, ‘Prediction-space knowledge markets for communication-efficient federated learning on multimedia tasks’, addresses these challenges by introducing KTA v2, a novel framework leveraging prediction-space knowledge distillation to substantially reduce communication costs while improving model accuracy. Through a client-client knowledge market and personalized soft targets, KTA v2 achieves state-of-the-art results on diverse multimedia datasets, significantly outperforming existing methods with orders of magnitude less communication overhead. Could this approach unlock truly scalable and practical federated learning for resource-constrained devices and massive datasets?

The Erosion of Centralization in Machine Learning

Conventional machine learning methodologies often demand the consolidation of data into centralized repositories, a practice increasingly fraught with difficulties. This centralization introduces substantial privacy risks, as sensitive user information becomes a single point of vulnerability to breaches and misuse. Beyond privacy, logistical hurdles abound – the sheer volume of data transfer required strains network bandwidth and incurs significant costs, while regulatory constraints, such as GDPR, further complicate the process of collecting and storing data in a single location. Consequently, the traditional approach is becoming unsustainable in an era defined by data proliferation, heightened privacy awareness, and the growing ubiquity of decentralized data sources. The need for alternatives that prioritize data locality and user privacy is becoming ever more pressing.

The proliferation of edge devices – smartphones, IoT sensors, and autonomous vehicles – coupled with increasing data localization regulations, is fundamentally reshaping machine learning practices. Previously, algorithms relied on consolidating data in centralized servers for training; however, this approach now faces practical and legal limitations. Moving computation to the edge-directly on the devices generating the data-not only addresses privacy concerns and reduces bandwidth requirements, but also unlocks the potential for real-time insights and responsiveness. This necessitates a paradigm shift towards distributed learning frameworks, where models are collaboratively trained across a network of decentralized devices, rather than relying on a single, central repository. This distributed approach presents unique challenges, but it is increasingly becoming essential for harnessing the full power of the data generated at the network’s edge and enabling a new generation of intelligent applications.

The decentralized nature of distributed learning introduces a critical obstacle: non-independent and identically distributed (Non-IID) data. Unlike traditional machine learning where data is often assumed to be drawn from a single distribution, edge devices – such as smartphones or IoT sensors – typically possess data reflecting highly individualized user behavior or environmental conditions. This means each device’s dataset is statistically different from others, violating the core assumptions of many machine learning algorithms. Consequently, models trained on such fragmented, heterogeneous data struggle to converge efficiently, potentially leading to biased or poorly generalized results. Addressing this Non-IID challenge requires sophisticated techniques – including data weighting, model aggregation strategies, and personalization methods – to ensure robust and reliable performance across the entire network of devices, and ultimately unlock the full potential of distributed intelligence.

Federated Learning: A Foundation for Distributed Intelligence

Federated Learning (FL) is a distributed machine learning approach that allows model training on a decentralized network of devices or servers holding local data samples, without exchanging those data samples. Instead of centralizing the training data, FL algorithms operate by distributing the model to each participating client. Each client then trains the model locally on its own dataset, generating model updates – typically gradients or model weights. These updates are then sent to a central server, where they are aggregated – often using techniques like Federated Averaging – to create an improved global model. This global model is then redistributed to the clients for further local training, iterating the process. The primary benefit is preserving data privacy and reducing communication costs, as raw data remains on the client devices.

Federated Averaging (FedAvg) is a foundational algorithm in federated learning that addresses the aggregation of model updates from decentralized clients. The process involves each client training a model locally on its dataset, then uploading the resulting model weights to a central server. The server then computes a weighted average of these weights – weighted by the number of data samples each client possesses – to create a new global model. This global model is then distributed back to the clients for the next round of training. Formally, the global model update can be expressed as $w_{t+1} = \sum_{k=1}^{K} \frac{n_k}{n} w_{k,t}$, where $w_{t}$ represents the global model at round $t$, $w_{k,t}$ is the local model update from client $k$, $n_k$ is the number of samples on client $k$, and $n = \sum_{k=1}^{K} n_k$ is the total number of samples across all clients. FedAvg serves as a common benchmark against which more advanced aggregation techniques are evaluated.

While Federated Averaging (FedAvg) serves as a foundational algorithm for federated learning, its performance is demonstrably affected by statistical heterogeneity – variations in data distributions across participating clients. Empirical results indicate that, under non-independent and identically distributed (non-IID) data conditions, FedAvg can achieve an accuracy of only 42.1% while requiring 4265.5 MB of communication overhead. This performance level suggests that simple averaging of model updates is often insufficient when client data exhibits substantial differences, motivating research into more sophisticated aggregation strategies and communication-efficient methods to mitigate the impact of data heterogeneity.

Using a communication budget of 0-8 MB, KTA v2 achieves comparable accuracy to Local, FedAvg, and FedProx on CIFAR-10 with α=0.5.

Knowledge Transfer: A Pathway to Efficient Federated Learning

Prediction-based Federated Learning (FL) represents a departure from traditional methods like FedAvg which rely on the sharing of model parameters. Instead, prediction-based FL focuses on exchanging model predictions as the primary form of knowledge transfer between participating clients and the central server. This approach allows for greater privacy, as raw data remains localized, and can potentially reduce communication overhead. Clients generate predictions on their local datasets, and these predictions – rather than the model weights themselves – are aggregated at the server. The server then uses these aggregated predictions to update a global model, or to provide feedback to the clients for further local training. This contrasts with parameter sharing, where the full model weights are transmitted, which can be bandwidth-intensive and raise privacy concerns.

FedMD employs a global teacher model, pre-trained on publicly available datasets, to enhance the training process of local client models in a federated learning system. This teacher model isn’t directly involved in prediction tasks; instead, it generates pseudo-labels for unlabeled data held by clients. These pseudo-labels, representing the teacher’s knowledge, are then used as additional training signals for the local models, effectively transferring knowledge from the publicly available data to improve performance on potentially limited or biased client datasets. The use of a pre-trained teacher model mitigates the need for extensive local data and facilitates knowledge distillation, guiding local training towards a more generalized and robust solution.

KTA v2 enhances federated learning through a prediction-space knowledge market, enabling communication-efficient personalization by exchanging model predictions rather than model parameters. This approach demonstrably reduces communication costs; benchmark results indicate an 1118x decrease in communication overhead compared to the FedAvg algorithm. Critically, this reduction in communication is achieved while maintaining or improving model accuracy across participating clients, offering a substantial efficiency gain for resource-constrained federated learning deployments.

KTA v2: A Detailed Mechanism for Efficient Knowledge Aggregation

KTA v2 utilizes a prediction-space knowledge market to aggregate predictions from client models. This process involves evaluating predictions on a shared, fixed Reference Set. Aggregation weights are determined by two primary factors: prediction accuracy on this Reference Set, and the similarity between a client’s predictions and the aggregated consensus. Higher accuracy and greater similarity to the current consensus result in a proportionally larger weight being assigned to a client’s contributions during the aggregation step. This weighted aggregation forms the updated global model, effectively prioritizing knowledge from clients demonstrating both competence and alignment with the existing model.

Optimization of the knowledge market within KTA v2 is achieved through Block-Coordinate Descent, an iterative method that updates parameters by optimizing one variable at a time while holding others fixed. This approach efficiently navigates the parameter space to maximize market performance. Simultaneously, Prediction-Space Regularization is implemented to mitigate client drift stemming from Non-IID data distributions. This regularization technique minimizes the divergence between client predictions and a global, aggregated prediction, effectively encouraging clients to converge towards a shared understanding and preventing individual models from diverging significantly. The combined effect of these two mechanisms improves the stability and efficiency of the federated learning process.

KTA v2 demonstrably improves communication efficiency while mitigating the effects of cross-client drift arising from non-independent and identically distributed (Non-IID) data. Specifically, when evaluated on the CIFAR-10 dataset utilizing a ResNet-18 model, KTA v2 achieves a reported accuracy of 57.7% while transmitting only 3.8 megabytes of data. This performance indicates a substantial reduction in communication overhead compared to traditional federated learning approaches, enabling viable training scenarios with limited bandwidth or high communication costs.

When utilizing the SimpleCNN model on the CIFAR-10 dataset, KTA v2 achieves an accuracy of 49.3%. This performance is attained while maintaining a communication overhead of 7.6 MB. This metric represents the total amount of data exchanged during the federated learning process, indicating KTA v2’s efficiency in transmitting model updates and aggregated knowledge between clients and the server.

KTA v2 demonstrates strong performance on heterogeneous datasets with limited communication overhead. Specifically, on the AG News text classification task, the system achieves 89.3% accuracy while transmitting only 3.1 MB of data. On the FEMNIST dataset, designed to simulate federated learning with non-IID data from mobile devices, KTA v2 attains 74.5% accuracy, a result comparable to that of standard Federated Averaging (FedAvg) and FedProx algorithms. These results indicate KTA v2’s ability to maintain competitive accuracy with significantly reduced communication costs across diverse data distributions.

Decreasing the Dirichlet parameter α during non-IID CIFAR-10 training with a SimpleCNN progressively reduces test accuracy, indicating increased sensitivity to label skew.

Impact and Future Directions: Charting the Course for Decentralized Intelligence

Rigorous evaluations of these federated learning techniques were conducted utilizing established datasets representing diverse machine learning challenges. The FEMNIST dataset, comprised of handwritten digits, assessed performance in a federated environment mirroring real-world user data distribution; meanwhile, the image classification benchmark, CIFAR-10, gauged the method’s ability to handle complex visual patterns across decentralized clients. Furthermore, the AG News dataset, focused on news topic classification, tested the techniques’ efficacy with text-based data. Consistent strong performance across these varied datasets – ranging from character recognition to image and text analysis – demonstrates the robustness and adaptability of the proposed federated learning approaches, validating their potential for broader application in decentralized machine learning scenarios.

The integration of established deep learning optimizations significantly bolsters the performance of federated learning models. Techniques like Batch Normalization, which stabilizes learning by reducing internal covariate shift, and the implementation of residual networks – specifically ResNet-18 – address the vanishing gradient problem common in deeper architectures. By normalizing layer inputs, Batch Normalization enables higher learning rates and faster convergence, while ResNet-18’s skip connections facilitate the training of more complex models without sacrificing accuracy. These enhancements are not merely incremental; they represent a crucial step towards achieving robust and reliable performance across diverse, decentralized datasets, ensuring that federated learning can effectively leverage the power of deep neural networks while preserving data privacy.

Recognizing the inherent diversity in data held by individual clients, personalized federated learning emerges as a critical advancement beyond traditional approaches. Standard federated learning often assumes a degree of data similarity, yet real-world scenarios frequently involve substantial variations – a phenomenon known as non-IID data. To address this, techniques like Moreau Envelopes and Dirichlet Distribution are employed to tailor model learning to each client’s specific data distribution. Moreau Envelopes facilitate the creation of personalized models by effectively smoothing the optimization landscape, while the Dirichlet Distribution allows for the strategic weighting of local model updates, ensuring that clients with unique data contribute appropriately to the global model. This personalization not only improves individual client performance but also enhances the robustness and generalization capabilities of the overall federated system, paving the way for more effective and equitable machine learning deployments across diverse populations and devices.

Continued research endeavors are increasingly focused on streamlining the communication protocols inherent in federated learning systems. Current methods often require substantial bandwidth, limiting scalability and practicality, particularly with resource-constrained devices. Investigations are underway to explore techniques like model compression, gradient sparsification, and asynchronous communication strategies to minimize data transfer without significantly compromising model accuracy. Simultaneously, a growing body of work addresses the challenges posed by Non-IID (non-independent and identically distributed) data, where each client possesses a unique data distribution. This necessitates developing algorithms robust to statistical heterogeneity, potentially through advanced personalization techniques or the implementation of methods that actively mitigate the effects of data imbalance. Successfully navigating these hurdles will be critical for deploying federated learning in real-world applications characterized by diverse and unevenly distributed data landscapes.

The pursuit of communication efficiency, central to KTA v2’s design, echoes a timeless sentiment. Blaise Pascal observed, “The necessity of defending one’s opinions has never been a sign of their validity.” Similarly, this framework minimizes superfluous data exchange – the ‘defense’ of redundant information – focusing instead on distilling essential predictive knowledge. The prediction-space knowledge market, by prioritizing the transfer of meaningful signals, embodies a principle of parsimony; it recognizes that true understanding isn’t measured by the volume of data, but by the clarity and precision of its message. This approach, akin to stripping away unnecessary complexity, ultimately enhances the robustness and scalability of federated learning, particularly within non-IID multimedia environments.

What’s Next?

The pursuit of communication efficiency in federated learning, as exemplified by this work, invariably circles back to the fundamental question of what truly needs to be transmitted. KTA v2’s prediction-space knowledge market offers a pragmatic, if not elegant, reduction. Yet, the notion of a ‘knowledge’ market implies a complete understanding of what constitutes valuable knowledge in the first place. The current formulation, effective as it is, treats predictions as proxies. A more direct encoding of model uncertainty, rather than simply the outputs, may reveal further compression opportunities. The simplicity is appealing, but a deeper consideration of information theory, beyond mere bandwidth constraints, seems warranted.

Heterogeneous data, the acknowledged nemesis of federated learning, continues to demand attention. While KTA v2 mitigates some of the challenges posed by non-IID distributions, the system still relies on a degree of overlap in learned features. The truly disparate – a collection of sensors measuring entirely different phenomena – remains a considerable hurdle. The focus must shift from simply averaging knowledge to actively translating it, a task bordering on genuine machine intelligence. Intuition suggests that code should be as self-evident as gravity, but translating between entirely different ‘languages’ is anything but.

Finally, the scaling limitations of these knowledge markets deserve scrutiny. The market mechanism itself introduces computational overhead. The benefits of reduced communication must demonstrably outweigh the costs of maintaining the market, particularly as the number of participating clients increases. Perfection is reached not when there is nothing more to add, but when there is nothing left to take away; the ultimate goal is a system that vanishes into the network, leaving only the learned model behind.

Original article: https://arxiv.org/pdf/2512.00841.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/